rlm@425: #+title: =CORTEX=
rlm@425: #+author: Robert McIntyre
rlm@425: #+email: rlm@mit.edu
rlm@425: #+description: Using embodied AI to facilitate Artificial Imagination.
rlm@425: #+keywords: AI, clojure, embodiment
rlm@451: #+LaTeX_CLASS_OPTIONS: [nofloat]
rlm@422: 
rlm@465: * COMMENT templates
rlm@470:    #+caption: 
rlm@470:    #+caption: 
rlm@470:    #+caption: 
rlm@470:    #+caption: 
rlm@470:    #+name: name
rlm@470:    #+begin_listing clojure
rlm@479:    #+BEGIN_SRC clojure
rlm@479:    #+END_SRC
rlm@470:    #+end_listing
rlm@465: 
rlm@470:    #+caption: 
rlm@470:    #+caption: 
rlm@470:    #+caption: 
rlm@470:    #+name: name
rlm@470:    #+ATTR_LaTeX: :width 10cm
rlm@470:    [[./images/aurellem-gray.png]]
rlm@470: 
rlm@470:     #+caption: 
rlm@470:     #+caption: 
rlm@470:     #+caption: 
rlm@470:     #+caption: 
rlm@470:     #+name: name
rlm@470:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@475:     #+END_SRC
rlm@470:     #+end_listing
rlm@470: 
rlm@470:     #+caption: 
rlm@470:     #+caption: 
rlm@470:     #+caption: 
rlm@470:     #+name: name
rlm@470:     #+ATTR_LaTeX: :width 10cm
rlm@470:     [[./images/aurellem-gray.png]]
rlm@470: 
rlm@564:     
rlm@541: * Empathy \& Embodiment: problem solving strategies
rlm@516: 
rlm@551:   By the time you have read this thesis, you will understand a novel
rlm@551:   approach to representing and recognizing physical actions using
rlm@551:   embodiment and empathy. You will also see one way to efficiently
rlm@551:   implement physical empathy for embodied creatures. Finally, you will
rlm@551:   become familiar with =CORTEX=, a system for designing and simulating
rlm@551:   creatures with rich senses, which I have designed as a library that
rlm@551:   you can use in your own research. Note that I /do not/ process video
rlm@551:   directly --- I start with knowledge of the positions of a creature's
rlm@551:   body parts and work from there.
rlm@437:   
rlm@516:   This is the core vision of my thesis: That one of the important ways
rlm@516:   in which we understand others is by imagining ourselves in their
rlm@561:   position and empathically feeling experiences relative to our own
rlm@516:   bodies. By understanding events in terms of our own previous
rlm@516:   corporeal experience, we greatly constrain the possibilities of what
rlm@516:   would otherwise be an unwieldy exponential search. This extra
rlm@516:   constraint can be the difference between easily understanding what
rlm@516:   is happening in a video and being completely lost in a sea of
rlm@516:   incomprehensible color and movement.
rlm@516:   
rlm@525: ** The problem: recognizing actions is hard!
rlm@511:    
rlm@551:    Examine figure \ref{cat-drink}. What is happening? As you, and
rlm@551:    indeed very young children, can easily determine, this is an image
rlm@551:    of drinking.
rlm@516: 
rlm@441:    #+caption: A cat drinking some water. Identifying this action is 
rlm@511:    #+caption: beyond the capabilities of existing computer vision systems.
rlm@551:    #+name: cat-drink
rlm@441:    #+ATTR_LaTeX: :width 7cm
rlm@441:    [[./images/cat-drinking.jpg]]
rlm@511:      
rlm@511:    Nevertheless, it is beyond the state of the art for a computer
rlm@516:    vision program to describe what's happening in this image. Part of
rlm@516:    the problem is that many computer vision systems focus on
rlm@516:    pixel-level details or comparisons to example images (such as
rlm@516:    \cite{volume-action-recognition}), but the 3D world is so variable
rlm@517:    that it is hard to describe the world in terms of possible images.
rlm@511: 
rlm@547:    In fact, the contents of a scene may have much less to do with
rlm@547:    pixel probabilities than with recognizing various affordances:
rlm@547:    things you can move, objects you can grasp, spaces that can be
rlm@547:    filled . For example, what processes might enable you to see the
rlm@547:    chair in figure \ref{hidden-chair}?
rlm@516: 
rlm@525:    #+caption: The chair in this image is quite obvious to humans, but 
rlm@525:    #+caption: it can't be found by any modern computer vision program.
rlm@441:    #+name: hidden-chair
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@441:    [[./images/fat-person-sitting-at-desk.jpg]]
rlm@511: 
rlm@441:    Finally, how is it that you can easily tell the difference between
rlm@551:    how the girl's /muscles/ are working in figure \ref{girl}?
rlm@441:    
rlm@441:    #+caption: The mysterious ``common sense'' appears here as you are able 
rlm@441:    #+caption: to discern the difference in how the girl's arm muscles
rlm@551:    #+caption: are activated between the two images. When you compare
rlm@551:    #+caption: these two images, do you feel something in your own arm
rlm@551:    #+caption: muscles?
rlm@441:    #+name: girl
rlm@448:    #+ATTR_LaTeX: :width 7cm
rlm@441:    [[./images/wall-push.png]]
rlm@437:   
rlm@441:    Each of these examples tells us something about what might be going
rlm@517:    on in our minds as we easily solve these recognition problems:
rlm@441:    
rlm@547:    - The hidden chair shows us that we are strongly triggered by cues
rlm@547:      relating to the position of human bodies, and that we can
rlm@547:      determine the overall physical configuration of a human body even
rlm@547:      if much of that body is occluded.
rlm@547: 
rlm@547:    - The picture of the girl pushing against the wall tells us that we
rlm@547:      have common sense knowledge about the kinetics of our own bodies.
rlm@547:      We know well how our muscles would have to work to maintain us in
rlm@547:      most positions, and we can easily project this self-knowledge to
rlm@547:      imagined positions triggered by images of the human body.
rlm@547: 
rlm@547:    - The cat tells us that imagination of some kind plays an important
rlm@547:      role in understanding actions. The question is: Can we be more
rlm@547:      precise about what sort of imagination is required to understand
rlm@547:      these actions?
rlm@517: 
rlm@511: ** A step forward: the sensorimotor-centered approach
rlm@516: 
rlm@511:    In this thesis, I explore the idea that our knowledge of our own
rlm@516:    bodies, combined with our own rich senses, enables us to recognize
rlm@516:    the actions of others.
rlm@516: 
rlm@516:    For example, I think humans are able to label the cat video as
rlm@516:    ``drinking'' because they imagine /themselves/ as the cat, and
rlm@516:    imagine putting their face up against a stream of water and
rlm@516:    sticking out their tongue. In that imagined world, they can feel
rlm@516:    the cool water hitting their tongue, and feel the water entering
rlm@516:    their body, and are able to recognize that /feeling/ as drinking.
rlm@516:    So, the label of the action is not really in the pixels of the
rlm@547:    image, but is found clearly in a simulation / recollection inspired
rlm@547:    by those pixels. An imaginative system, having been trained on
rlm@547:    drinking and non-drinking examples and learning that the most
rlm@551:    important component of drinking is the feeling of water flowing
rlm@547:    down one's throat, would analyze a video of a cat drinking in the
rlm@547:    following manner:
rlm@516:    
rlm@516:    1. Create a physical model of the video by putting a ``fuzzy''
rlm@516:       model of its own body in place of the cat. Possibly also create
rlm@516:       a simulation of the stream of water.
rlm@516: 
rlm@551:    2. Play out this simulated scene and generate imagined sensory
rlm@516:       experience. This will include relevant muscle contractions, a
rlm@516:       close up view of the stream from the cat's perspective, and most
rlm@517:       importantly, the imagined feeling of water entering the mouth.
rlm@517:       The imagined sensory experience can come from a simulation of
rlm@517:       the event, but can also be pattern-matched from previous,
rlm@517:       similar embodied experience.
rlm@516: 
rlm@516:    3. The action is now easily identified as drinking by the sense of
rlm@516:       taste alone. The other senses (such as the tongue moving in and
rlm@516:       out) help to give plausibility to the simulated action. Note that
rlm@516:       the sense of vision, while critical in creating the simulation,
rlm@516:       is not critical for identifying the action from the simulation.
rlm@516: 
rlm@516:    For the chair examples, the process is even easier:
rlm@516: 
rlm@516:     1. Align a model of your body to the person in the image.
rlm@516: 
rlm@516:     2. Generate proprioceptive sensory data from this alignment.
rlm@516:   
rlm@516:     3. Use the imagined proprioceptive data as a key to lookup related
rlm@517:        sensory experience associated with that particular proprioceptive
rlm@516:        feeling.
rlm@516: 
rlm@516:     4. Retrieve the feeling of your bottom resting on a surface, your
rlm@516:        knees bent, and your leg muscles relaxed.
rlm@516: 
rlm@516:     5. This sensory information is consistent with your =sitting?=
rlm@516:        sensory predicate, so you (and the entity in the image) must be
rlm@516:        sitting.
rlm@516: 
rlm@516:     6. There must be a chair-like object since you are sitting.
rlm@516: 
rlm@516:    Empathy offers yet another alternative to the age-old AI
rlm@516:    representation question: ``What is a chair?'' --- A chair is the
rlm@516:    feeling of sitting!
rlm@516: 
rlm@516:    One powerful advantage of empathic problem solving is that it
rlm@516:    factors the action recognition problem into two easier problems. To
rlm@516:    use empathy, you need an /aligner/, which takes the video and a
rlm@516:    model of your body, and aligns the model with the video. Then, you
rlm@516:    need a /recognizer/, which uses the aligned model to interpret the
rlm@516:    action. The power in this method lies in the fact that you describe
rlm@521:    all actions from a body-centered viewpoint. You are less tied to
rlm@516:    the particulars of any visual representation of the actions. If you
rlm@516:    teach the system what ``running'' is, and you have a good enough
rlm@516:    aligner, the system will from then on be able to recognize running
rlm@547:    from any point of view -- even strange points of view like above or
rlm@516:    underneath the runner. This is in contrast to action recognition
rlm@516:    schemes that try to identify actions using a non-embodied approach.
rlm@516:    If these systems learn about running as viewed from the side, they
rlm@516:    will not automatically be able to recognize running from any other
rlm@516:    viewpoint.
rlm@516: 
rlm@516:    Another powerful advantage is that using the language of multiple
rlm@547:    body-centered rich senses to describe body-centered actions offers
rlm@547:    a massive boost in descriptive capability. Consider how difficult
rlm@547:    it would be to compose a set of HOG (Histogram of Oriented
rlm@547:    Gradients) filters to describe the action of a simple worm-creature
rlm@547:    ``curling'' so that its head touches its tail, and then behold the
rlm@547:    simplicity of describing thus action in a language designed for the
rlm@547:    task (listing \ref{grand-circle-intro}):
rlm@516: 
rlm@517:    #+caption: Body-centered actions are best expressed in a body-centered 
rlm@516:    #+caption: language. This code detects when the worm has curled into a 
rlm@516:    #+caption: full circle. Imagine how you would replicate this functionality
rlm@516:    #+caption: using low-level pixel features such as HOG filters!
rlm@516:    #+name: grand-circle-intro
rlm@516:    #+begin_listing clojure
rlm@516:    #+begin_src clojure
rlm@516: (defn grand-circle?
rlm@516:   "Does the worm form a majestic circle (one end touching the other)?"
rlm@516:   [experiences]
rlm@516:   (and (curled? experiences)
rlm@516:        (let [worm-touch (:touch (peek experiences))
rlm@516:              tail-touch (worm-touch 0)
rlm@516:              head-touch (worm-touch 4)]
rlm@516:          (and (< 0.2 (contact worm-segment-bottom-tip tail-touch))
rlm@516:               (< 0.2 (contact worm-segment-top-tip    head-touch))))))
rlm@516:    #+end_src
rlm@516:    #+end_listing
rlm@516: 
rlm@517: ** =EMPATH= recognizes actions using empathy
rlm@517: 
rlm@517:    Exploring these ideas further demands a concrete implementation, so
rlm@517:    first, I built a system for constructing virtual creatures with
rlm@511:    physiologically plausible sensorimotor systems and detailed
rlm@551:    environments. The result is =CORTEX=, which I describe in chapter
rlm@517:    \ref{sec-2}.
rlm@511: 
rlm@511:    Next, I wrote routines which enabled a simple worm-like creature to
rlm@511:    infer the actions of a second worm-like creature, using only its
rlm@511:    own prior sensorimotor experiences and knowledge of the second
rlm@511:    worm's joint positions. This program, =EMPATH=, is described in
rlm@551:    chapter \ref{sec-3}. It's main components are:
rlm@517: 
rlm@517:    - Embodied Action Definitions :: Many otherwise complicated actions
rlm@517:         are easily described in the language of a full suite of
rlm@517:         body-centered, rich senses and experiences. For example,
rlm@551:         drinking is the feeling of water flowing down your throat, and
rlm@448:         cooling your insides. It's often accompanied by bringing your
rlm@448:         hand close to your face, or bringing your face close to water.
rlm@448:         Sitting down is the feeling of bending your knees, activating
rlm@448:         your quadriceps, then feeling a surface with your bottom and
rlm@448:         relaxing your legs. These body-centered action descriptions
rlm@448:         can be either learned or hard coded.
rlm@517: 
rlm@517:    - Guided Play      :: The creature moves around and experiences the
rlm@517:         world through its unique perspective. As the creature moves,
rlm@517:         it gathers experiences that satisfy the embodied action
rlm@517:         definitions. 
rlm@517: 
rlm@562:    - Posture Imitation :: When trying to interpret a video or image,
rlm@448:         the creature takes a model of itself and aligns it with
rlm@517:         whatever it sees. This alignment might even cross species, as
rlm@448:         when humans try to align themselves with things like ponies,
rlm@448:         dogs, or other humans with a different body type.
rlm@517: 
rlm@517:    - Empathy          :: The alignment triggers associations with
rlm@448:         sensory data from prior experiences. For example, the
rlm@448:         alignment itself easily maps to proprioceptive data. Any
rlm@448:         sounds or obvious skin contact in the video can to a lesser
rlm@517:         extent trigger previous experience keyed to hearing or touch.
rlm@517:         Segments of previous experiences gained from play are stitched
rlm@517:         together to form a coherent and complete sensory portrait of
rlm@517:         the scene.
rlm@517: 
rlm@547:    - Recognition :: With the scene described in terms of remembered
rlm@547:         first person sensory events, the creature can now run its
rlm@547:         action-definition programs (such as the one in listing
rlm@547:         \ref{grand-circle-intro}) on this synthesized sensory data,
rlm@517:         just as it would if it were actually experiencing the scene
rlm@517:         first-hand. If previous experience has been accurately
rlm@447:         retrieved, and if it is analogous enough to the scene, then
rlm@447:         the creature will correctly identify the action in the scene.
rlm@441: 
rlm@562:    My program =EMPATH= uses this empathic problem solving technique
rlm@441:    to interpret the actions of a simple, worm-like creature. 
rlm@437:    
rlm@441:    #+caption: The worm performs many actions during free play such as 
rlm@441:    #+caption: curling, wiggling, and resting.
rlm@441:    #+name: worm-intro
rlm@446:    #+ATTR_LaTeX: :width 15cm
rlm@445:    [[./images/worm-intro-white.png]]
rlm@437: 
rlm@462:    #+caption: =EMPATH= recognized and classified each of these 
rlm@462:    #+caption: poses by inferring the complete sensory experience 
rlm@462:    #+caption: from proprioceptive data.
rlm@441:    #+name: worm-recognition-intro
rlm@446:    #+ATTR_LaTeX: :width 15cm
rlm@445:    [[./images/worm-poses.png]]
rlm@441:    
rlm@517: *** Main Results 
rlm@517: 
rlm@521:    - After one-shot supervised training, =EMPATH= was able to
rlm@521:      recognize a wide variety of static poses and dynamic
rlm@521:      actions---ranging from curling in a circle to wiggling with a
rlm@521:      particular frequency --- with 95\% accuracy.
rlm@517: 
rlm@517:    - These results were completely independent of viewing angle
rlm@517:      because the underlying body-centered language fundamentally is
rlm@517:      independent; once an action is learned, it can be recognized
rlm@517:      equally well from any viewing angle.
rlm@517: 
rlm@517:    - =EMPATH= is surprisingly short; the sensorimotor-centered
rlm@517:      language provided by =CORTEX= resulted in extremely economical
rlm@517:      recognition routines --- about 500 lines in all --- suggesting
rlm@517:      that such representations are very powerful, and often
rlm@517:      indispensable for the types of recognition tasks considered here.
rlm@517: 
rlm@551:    - For expediency's sake, I relied on direct knowledge of joint
rlm@551:      positions in this proof of concept. However, I believe that the
rlm@551:      structure of =EMPATH= and =CORTEX= will make future work to
rlm@551:      enable video analysis much easier than it would otherwise be.
rlm@517: 
rlm@563: ** =EMPATH= is built on =CORTEX=, a creature builder.
rlm@435: 
rlm@448:    I built =CORTEX= to be a general AI research platform for doing
rlm@448:    experiments involving multiple rich senses and a wide variety and
rlm@448:    number of creatures. I intend it to be useful as a library for many
rlm@462:    more projects than just this thesis. =CORTEX= was necessary to meet
rlm@462:    a need among AI researchers at CSAIL and beyond, which is that
rlm@547:    people often will invent wonderful ideas that are best expressed in
rlm@547:    the language of creatures and senses, but in order to explore those
rlm@448:    ideas they must first build a platform in which they can create
rlm@448:    simulated creatures with rich senses! There are many ideas that
rlm@547:    would be simple to execute (such as =EMPATH= or Larson's
rlm@547:    self-organizing maps (\cite{larson-symbols})), but attached to them
rlm@547:    is the multi-month effort to make a good creature simulator. Often,
rlm@547:    that initial investment of time proves to be too much, and the
rlm@547:    project must make do with a lesser environment or be abandoned
rlm@547:    entirely.
rlm@435: 
rlm@448:    =CORTEX= is well suited as an environment for embodied AI research
rlm@448:    for three reasons:
rlm@448: 
rlm@547:    - You can design new creatures using Blender (\cite{blender}), a
rlm@551:      popular, free 3D modeling program. Each sense can be specified
rlm@562:      using special Blender nodes with biologically inspired
rlm@551:      parameters. You need not write any code to create a creature, and
rlm@567:      can use a wide library of pre-existing Blender models as a base
rlm@551:      for your own creatures.
rlm@448: 
rlm@511:    - =CORTEX= implements a wide variety of senses: touch,
rlm@448:      proprioception, vision, hearing, and muscle tension. Complicated
rlm@545:      senses like touch and vision involve multiple sensory elements
rlm@448:      embedded in a 2D surface. You have complete control over the
rlm@448:      distribution of these sensor elements through the use of simple
rlm@551:      image files. =CORTEX= implements more comprehensive hearing than
rlm@551:      any other creature simulation system available.
rlm@448: 
rlm@448:    - =CORTEX= supports any number of creatures and any number of
rlm@517:      senses. Time in =CORTEX= dilates so that the simulated creatures
rlm@517:      always perceive a perfectly smooth flow of time, regardless of
rlm@448:      the actual computational load.
rlm@448: 
rlm@517:    =CORTEX= is built on top of =jMonkeyEngine3=
rlm@517:    (\cite{jmonkeyengine}), which is a video game engine designed to
rlm@517:    create cross-platform 3D desktop games. =CORTEX= is mainly written
rlm@551:    in clojure, a dialect of =LISP= that runs on the Java Virtual
rlm@551:    Machine (JVM). The API for creating and simulating creatures and
rlm@517:    senses is entirely expressed in clojure, though many senses are
rlm@517:    implemented at the layer of jMonkeyEngine or below. For example,
rlm@517:    for the sense of hearing I use a layer of clojure code on top of a
rlm@517:    layer of java JNI bindings that drive a layer of =C++= code which
rlm@517:    implements a modified version of =OpenAL= to support multiple
rlm@517:    listeners. =CORTEX= is the only simulation environment that I know
rlm@517:    of that can support multiple entities that can each hear the world
rlm@517:    from their own perspective. Other senses also require a small layer
rlm@517:    of Java code. =CORTEX= also uses =bullet=, a physics simulator
rlm@517:    written in =C=.
rlm@448: 
rlm@516:    #+caption: Here is the worm from figure \ref{worm-intro} modeled 
rlm@516:    #+caption: in Blender, a free 3D-modeling program. Senses and 
rlm@516:    #+caption: joints are described using special nodes in Blender.
rlm@518:    #+name: worm-recognition-intro-2
rlm@448:    #+ATTR_LaTeX: :width 12cm
rlm@448:    [[./images/blender-worm.png]]
rlm@448: 
rlm@521:    Here are some things I anticipate that =CORTEX= might be used for:
rlm@449: 
rlm@449:    - exploring new ideas about sensory integration
rlm@449:    - distributed communication among swarm creatures
rlm@449:    - self-learning using free exploration, 
rlm@449:    - evolutionary algorithms involving creature construction
rlm@517:    - exploration of exotic senses and effectors that are not possible
rlm@517:      in the real world (such as telekinesis or a semantic sense)
rlm@449:    - imagination using subworlds
rlm@449: 
rlm@451:    During one test with =CORTEX=, I created 3,000 creatures each with
rlm@551:    its own independent senses and ran them all at only 1/80 real time.
rlm@551:    In another test, I created a detailed model of my own hand,
rlm@448:    equipped with a realistic distribution of touch (more sensitive at
rlm@448:    the fingertips), as well as eyes and ears, and it ran at around 1/4
rlm@451:    real time.
rlm@448: 
rlm@451: #+BEGIN_LaTeX
rlm@449:    \begin{sidewaysfigure}
rlm@566:    \includegraphics[width=8.5in]{images/full-hand.png}
rlm@451:    \caption{
rlm@451:    I modeled my own right hand in Blender and rigged it with all the
rlm@451:    senses that {\tt CORTEX} supports. My simulated hand has a
rlm@451:    biologically inspired distribution of touch sensors. The senses are
rlm@562:    displayed on the right (the red/black squares are raw sensory output), 
rlm@562:    and the simulation is displayed on the
rlm@451:    left. Notice that my hand is curling its fingers, that it can see
rlm@451:    its own finger from the eye in its palm, and that it can feel its
rlm@451:    own thumb touching its palm.}
rlm@449:    \end{sidewaysfigure}
rlm@451: #+END_LaTeX
rlm@448: 
rlm@558: * Designing =CORTEX=
rlm@551: 
rlm@551:   In this chapter, I outline the design decisions that went into
rlm@516:   making =CORTEX=, along with some details about its implementation.
rlm@516:   (A practical guide to getting started with =CORTEX=, which skips
rlm@516:   over the history and implementation details presented here, is
rlm@516:   provided in an appendix at the end of this thesis.)
rlm@511: 
rlm@511:   Throughout this project, I intended for =CORTEX= to be flexible and
rlm@511:   extensible enough to be useful for other researchers who want to
rlm@547:   test ideas of their own. To this end, wherever I have had to make
rlm@517:   architectural choices about =CORTEX=, I have chosen to give as much
rlm@511:   freedom to the user as possible, so that =CORTEX= may be used for
rlm@517:   things I have not foreseen.
rlm@511: 
rlm@511: ** Building in simulation versus reality
rlm@517:    The most important architectural decision of all is the choice to
rlm@517:    use a computer-simulated environment in the first place! The world
rlm@462:    is a vast and rich place, and for now simulations are a very poor
rlm@462:    reflection of its complexity. It may be that there is a significant
rlm@517:    qualitative difference between dealing with senses in the real
rlm@517:    world and dealing with pale facsimiles of them in a simulation
rlm@547:    (\cite{brooks-representation}). What are the advantages and
rlm@514:    disadvantages of a simulation vs. reality?
rlm@515:    
rlm@462: *** Simulation
rlm@462: 
rlm@462:     The advantages of virtual reality are that when everything is a
rlm@462:     simulation, experiments in that simulation are absolutely
rlm@547:     reproducible. It's also easier to change the creature and
rlm@547:     environment to explore new situations and different sensory
rlm@547:     combinations.
rlm@462: 
rlm@462:     If the world is to be simulated on a computer, then not only do
rlm@547:     you have to worry about whether the creature's senses are rich
rlm@462:     enough to learn from the world, but whether the world itself is
rlm@462:     rendered with enough detail and realism to give enough working
rlm@547:     material to the creature's senses. To name just a few
rlm@462:     difficulties facing modern physics simulators: destructibility of
rlm@462:     the environment, simulation of water/other fluids, large areas,
rlm@462:     nonrigid bodies, lots of objects, smoke. I don't know of any
rlm@547:     computer simulation that would allow a creature to take a rock
rlm@462:     and grind it into fine dust, then use that dust to make a clay
rlm@462:     sculpture, at least not without spending years calculating the
rlm@462:     interactions of every single small grain of dust. Maybe a
rlm@462:     simulated world with today's limitations doesn't provide enough
rlm@462:     richness for real intelligence to evolve.
rlm@462: 
rlm@462: *** Reality
rlm@462: 
rlm@462:     The other approach for playing with senses is to hook your
rlm@462:     software up to real cameras, microphones, robots, etc., and let it
rlm@462:     loose in the real world. This has the advantage of eliminating
rlm@462:     concerns about simulating the world at the expense of increasing
rlm@462:     the complexity of implementing the senses. Instead of just
rlm@462:     grabbing the current rendered frame for processing, you have to
rlm@462:     use an actual camera with real lenses and interact with photons to
rlm@547:     get an image. It is much harder to change the creature, which is
rlm@462:     now partly a physical robot of some sort, since doing so involves
rlm@462:     changing things around in the real world instead of modifying
rlm@462:     lines of code. While the real world is very rich and definitely
rlm@547:     provides enough stimulation for intelligence to develop (as
rlm@547:     evidenced by our own existence), it is also uncontrollable in the
rlm@462:     sense that a particular situation cannot be recreated perfectly or
rlm@547:     saved for later use. It is harder to conduct Science because it is
rlm@462:     harder to repeat an experiment. The worst thing about using the
rlm@462:     real world instead of a simulation is the matter of time. Instead
rlm@462:     of simulated time you get the constant and unstoppable flow of
rlm@462:     real time. This severely limits the sorts of software you can use
rlm@525:     to program an AI, because all sense inputs must be handled in real
rlm@462:     time. Complicated ideas may have to be implemented in hardware or
rlm@462:     may simply be impossible given the current speed of our
rlm@462:     processors. Contrast this with a simulation, in which the flow of
rlm@462:     time in the simulated world can be slowed down to accommodate the
rlm@547:     limitations of the creature's programming. In terms of cost, doing
rlm@547:     everything in software is far cheaper than building custom
rlm@462:     real-time hardware. All you need is a laptop and some patience.
rlm@515:     
rlm@516: ** Simulated time enables rapid prototyping \& simple programs
rlm@435: 
rlm@462:    I envision =CORTEX= being used to support rapid prototyping and
rlm@462:    iteration of ideas. Even if I could put together a well constructed
rlm@462:    kit for creating robots, it would still not be enough because of
rlm@462:    the scourge of real-time processing. Anyone who wants to test their
rlm@462:    ideas in the real world must always worry about getting their
rlm@465:    algorithms to run fast enough to process information in real time.
rlm@465:    The need for real time processing only increases if multiple senses
rlm@465:    are involved. In the extreme case, even simple algorithms will have
rlm@465:    to be accelerated by ASIC chips or FPGAs, turning what would
rlm@517:    otherwise be a few lines of code and a 10x speed penalty into a
rlm@465:    multi-month ordeal. For this reason, =CORTEX= supports
rlm@547:    /time-dilation/, which scales back the framerate of the simulation
rlm@547:    in proportion to the amount of processing each frame. From the
rlm@547:    perspective of the creatures inside the simulation, time always
rlm@547:    appears to flow at a constant rate, regardless of how complicated
rlm@547:    the environment becomes or how many creatures are in the
rlm@547:    simulation. The cost is that =CORTEX= can sometimes run slower than
rlm@548:    real time. Time dilation works both ways, however --- simulations
rlm@547:    of very simple creatures in =CORTEX= generally run at 40x real-time
rlm@547:    on my machine!
rlm@462: 
rlm@511: ** All sense organs are two-dimensional surfaces
rlm@514: 
rlm@468:    If =CORTEX= is to support a wide variety of senses, it would help
rlm@547:    to have a better understanding of what a sense actually is! While
rlm@547:    vision, touch, and hearing all seem like they are quite different
rlm@547:    things, I was surprised to learn during the course of this thesis
rlm@547:    that they (and all physical senses) can be expressed as exactly the
rlm@547:    same mathematical object!
rlm@468: 
rlm@468:    Human beings are three-dimensional objects, and the nerves that
rlm@468:    transmit data from our various sense organs to our brain are
rlm@468:    essentially one-dimensional. This leaves up to two dimensions in
rlm@468:    which our sensory information may flow. For example, imagine your
rlm@468:    skin: it is a two-dimensional surface around a three-dimensional
rlm@468:    object (your body). It has discrete touch sensors embedded at
rlm@468:    various points, and the density of these sensors corresponds to the
rlm@468:    sensitivity of that region of skin. Each touch sensor connects to a
rlm@468:    nerve, all of which eventually are bundled together as they travel
rlm@468:    up the spinal cord to the brain. Intersect the spinal nerves with a
rlm@468:    guillotining plane and you will see all of the sensory data of the
rlm@468:    skin revealed in a roughly circular two-dimensional image which is
rlm@468:    the cross section of the spinal cord. Points on this image that are
rlm@468:    close together in this circle represent touch sensors that are
rlm@468:    /probably/ close together on the skin, although there is of course
rlm@468:    some cutting and rearrangement that has to be done to transfer the
rlm@468:    complicated surface of the skin onto a two dimensional image.
rlm@468: 
rlm@468:    Most human senses consist of many discrete sensors of various
rlm@468:    properties distributed along a surface at various densities. For
rlm@468:    skin, it is Pacinian corpuscles, Meissner's corpuscles, Merkel's
rlm@547:    disks, and Ruffini's endings (\cite{textbook901}), which detect
rlm@517:    pressure and vibration of various intensities. For ears, it is the
rlm@517:    stereocilia distributed along the basilar membrane inside the
rlm@517:    cochlea; each one is sensitive to a slightly different frequency of
rlm@517:    sound. For eyes, it is rods and cones distributed along the surface
rlm@517:    of the retina. In each case, we can describe the sense with a
rlm@517:    surface and a distribution of sensors along that surface.
rlm@468: 
rlm@525:    In fact, almost every human sense can be effectively described in
rlm@525:    terms of a surface containing embedded sensors. If the sense had
rlm@525:    any more dimensions, then there wouldn't be enough room in the
rlm@547:    spinal cord to transmit the information!
rlm@468: 
rlm@468:    Therefore, =CORTEX= must support the ability to create objects and
rlm@468:    then be able to ``paint'' points along their surfaces to describe
rlm@468:    each sense. 
rlm@468: 
rlm@468:    Fortunately this idea is already a well known computer graphics
rlm@548:    technique called /UV-mapping/. In UV-mapping, the three-dimensional
rlm@547:    surface of a model is cut and smooshed until it fits on a
rlm@547:    two-dimensional image. You paint whatever you want on that image,
rlm@547:    and when the three-dimensional shape is rendered in a game the
rlm@547:    smooshing and cutting is reversed and the image appears on the
rlm@547:    three-dimensional object.
rlm@468: 
rlm@468:    To make a sense, interpret the UV-image as describing the
rlm@563:    distribution of that senses' sensors. To get different types of
rlm@468:    sensors, you can either use a different color for each type of
rlm@468:    sensor, or use multiple UV-maps, each labeled with that sensor
rlm@468:    type. I generally use a white pixel to mean the presence of a
rlm@468:    sensor and a black pixel to mean the absence of a sensor, and use
rlm@563:    one UV-map for each sensor-type within a given sense.
rlm@468: 
rlm@468:    #+CAPTION: The UV-map for an elongated icososphere. The white
rlm@468:    #+caption: dots each represent a touch sensor. They are dense 
rlm@468:    #+caption: in the regions that describe the tip of the finger, 
rlm@468:    #+caption: and less dense along the dorsal side of the finger 
rlm@468:    #+caption: opposite the tip.
rlm@468:    #+name: finger-UV
rlm@468:    #+ATTR_latex: :width 10cm
rlm@468:    [[./images/finger-UV.png]]
rlm@468: 
rlm@563:    #+caption: Ventral side of the UV-mapped finger. Note the 
rlm@468:    #+caption: density of touch sensors at the tip.
rlm@468:    #+name: finger-side-view
rlm@468:    #+ATTR_LaTeX: :width 10cm
rlm@468:    [[./images/finger-1.png]]
rlm@468: 
rlm@507: ** Video game engines provide ready-made physics and shading
rlm@462:    
rlm@462:    I did not need to write my own physics simulation code or shader to
rlm@462:    build =CORTEX=. Doing so would lead to a system that is impossible
rlm@462:    for anyone but myself to use anyway. Instead, I use a video game
rlm@517:    engine as a base and modify it to accommodate the additional needs
rlm@462:    of =CORTEX=. Video game engines are an ideal starting point to
rlm@462:    build =CORTEX=, because they are not far from being creature
rlm@463:    building systems themselves.
rlm@462:    
rlm@462:    First off, general purpose video game engines come with a physics
rlm@462:    engine and lighting / sound system. The physics system provides
rlm@462:    tools that can be co-opted to serve as touch, proprioception, and
rlm@563:    muscles. Because some games support split screen views, a good
rlm@563:    video game engine will allow you to efficiently create multiple
rlm@563:    cameras in the simulated world that can be used as eyes. Video game
rlm@563:    systems offer integrated asset management for things like textures
rlm@563:    and creature models, providing an avenue for defining creatures.
rlm@563:    They also understand UV-mapping, because this technique is used to
rlm@563:    apply a texture to a model. Finally, because video game engines
rlm@563:    support a large number of developers, as long as =CORTEX= doesn't
rlm@563:    stray too far from the base system, other researchers can turn to
rlm@563:    this community for help when doing their research.
rlm@463:    
rlm@507: ** =CORTEX= is based on jMonkeyEngine3
rlm@463: 
rlm@463:    While preparing to build =CORTEX= I studied several video game
rlm@463:    engines to see which would best serve as a base. The top contenders
rlm@463:    were:
rlm@463: 
rlm@547:    - [[http://www.idsoftware.com][Quake II]]/[[http://www.bytonic.de/html/jake2.html][Jake2]] :: The Quake II engine was designed by ID software
rlm@547:         in 1997. All the source code was released by ID software into
rlm@547:         the Public Domain several years ago, and as a result it has
rlm@547:         been ported to many different languages. This engine was
rlm@547:         famous for its advanced use of realistic shading and it had
rlm@547:         decent and fast physics simulation. The main advantage of the
rlm@547:         Quake II engine is its simplicity, but I ultimately rejected
rlm@547:         it because the engine is too tied to the concept of a
rlm@463:         first-person shooter game. One of the problems I had was that
rlm@463:         there does not seem to be any easy way to attach multiple
rlm@463:         cameras to a single character. There are also several physics
rlm@463:         clipping issues that are corrected in a way that only applies
rlm@463:         to the main character and do not apply to arbitrary objects.
rlm@463: 
rlm@463:    - [[http://source.valvesoftware.com/][Source Engine]]     :: The Source Engine evolved from the Quake II
rlm@463:         and Quake I engines and is used by Valve in the Half-Life
rlm@463:         series of games. The physics simulation in the Source Engine
rlm@463:         is quite accurate and probably the best out of all the engines
rlm@463:         I investigated. There is also an extensive community actively
rlm@463:         working with the engine. However, applications that use the
rlm@463:         Source Engine must be written in C++, the code is not open, it
rlm@463:         only runs on Windows, and the tools that come with the SDK to
rlm@463:         handle models and textures are complicated and awkward to use.
rlm@463: 
rlm@463:    -  [[http://jmonkeyengine.com/][jMonkeyEngine3]] :: jMonkeyEngine3 is a new library for creating
rlm@463:         games in Java. It uses OpenGL to render to the screen and uses
rlm@463:         screengraphs to avoid drawing things that do not appear on the
rlm@463:         screen. It has an active community and several games in the
rlm@463:         pipeline. The engine was not built to serve any particular
rlm@463:         game but is instead meant to be used for any 3D game. 
rlm@463: 
rlm@521:    I chose jMonkeyEngine3 because it had the most features out of all
rlm@521:    the free projects I looked at, and because I could then write my
rlm@521:    code in clojure, an implementation of =LISP= that runs on the JVM.
rlm@435: 
rlm@507: ** =CORTEX= uses Blender to create creature models
rlm@435: 
rlm@464:    For the simple worm-like creatures I will use later on in this
rlm@464:    thesis, I could define a simple API in =CORTEX= that would allow
rlm@464:    one to create boxes, spheres, etc., and leave that API as the sole
rlm@464:    way to create creatures. However, for =CORTEX= to truly be useful
rlm@468:    for other projects, it needs a way to construct complicated
rlm@464:    creatures. If possible, it would be nice to leverage work that has
rlm@464:    already been done by the community of 3D modelers, or at least
rlm@517:    enable people who are talented at modeling but not programming to
rlm@468:    design =CORTEX= creatures.
rlm@464: 
rlm@547:    Therefore I use Blender, a free 3D modeling program, as the main
rlm@464:    way to create creatures in =CORTEX=. However, the creatures modeled
rlm@464:    in Blender must also be simple to simulate in jMonkeyEngine3's game
rlm@468:    engine, and must also be easy to rig with =CORTEX='s senses. I
rlm@547:    accomplish this with extensive use of Blender's ``empty nodes.''
rlm@464: 
rlm@468:    Empty nodes have no mass, physical presence, or appearance, but
rlm@468:    they can hold metadata and have names. I use a tree structure of
rlm@468:    empty nodes to specify senses in the following manner:
rlm@468: 
rlm@468:    - Create a single top-level empty node whose name is the name of
rlm@468:      the sense.
rlm@468:    - Add empty nodes which each contain meta-data relevant to the
rlm@468:      sense, including a UV-map describing the number/distribution of
rlm@468:      sensors if applicable.
rlm@468:    - Make each empty-node the child of the top-level node.
rlm@468:      
rlm@517:    #+caption: An example of annotating a creature model with empty
rlm@468:    #+caption: nodes to describe the layout of senses. There are 
rlm@468:    #+caption: multiple empty nodes which each describe the position
rlm@468:    #+caption: of muscles, ears, eyes, or joints.
rlm@468:    #+name: sense-nodes
rlm@468:    #+ATTR_LaTeX: :width 10cm
rlm@468:    [[./images/empty-sense-nodes.png]]
rlm@468: 
rlm@508: ** Bodies are composed of segments connected by joints
rlm@468: 
rlm@468:    Blender is a general purpose animation tool, which has been used in
rlm@468:    the past to create high quality movies such as Sintel
rlm@547:    (\cite{blender}). Though Blender can model and render even
rlm@547:    complicated things like water, it is crucial to keep models that
rlm@547:    are meant to be simulated as creatures simple. =Bullet=, which
rlm@547:    =CORTEX= uses though jMonkeyEngine3, is a rigid-body physics
rlm@547:    system. This offers a compromise between the expressiveness of a
rlm@547:    game level and the speed at which it can be simulated, and it means
rlm@547:    that creatures should be naturally expressed as rigid components
rlm@547:    held together by joint constraints.
rlm@468: 
rlm@517:    But humans are more like a squishy bag wrapped around some hard
rlm@517:    bones which define the overall shape. When we move, our skin bends
rlm@517:    and stretches to accommodate the new positions of our bones.
rlm@468: 
rlm@468:    One way to make bodies composed of rigid pieces connected by joints
rlm@468:    /seem/ more human-like is to use an /armature/, (or /rigging/)
rlm@468:    system, which defines a overall ``body mesh'' and defines how the
rlm@468:    mesh deforms as a function of the position of each ``bone'' which
rlm@468:    is a standard rigid body. This technique is used extensively to
rlm@468:    model humans and create realistic animations. It is not a good
rlm@517:    technique for physical simulation because it is a lie -- the skin
rlm@517:    is not a physical part of the simulation and does not interact with
rlm@517:    any objects in the world or itself. Objects will pass right though
rlm@517:    the skin until they come in contact with the underlying bone, which
rlm@517:    is a physical object. Without simulating the skin, the sense of
rlm@517:    touch has little meaning, and the creature's own vision will lie to
rlm@517:    it about the true extent of its body. Simulating the skin as a
rlm@517:    physical object requires some way to continuously update the
rlm@517:    physical model of the skin along with the movement of the bones,
rlm@517:    which is unacceptably slow compared to rigid body simulation.
rlm@468: 
rlm@547:    Therefore, instead of using the human-like ``bony meatbag''
rlm@547:    approach, I decided to base my body plans on multiple solid objects
rlm@547:    that are connected by joints, inspired by the robot =EVE= from the
rlm@547:    movie WALL-E.
rlm@464:    
rlm@464:    #+caption: =EVE= from the movie WALL-E.  This body plan turns 
rlm@464:    #+caption: out to be much better suited to my purposes than a more 
rlm@464:    #+caption: human-like one.
rlm@465:    #+ATTR_LaTeX: :width 10cm
rlm@464:    [[./images/Eve.jpg]]
rlm@464: 
rlm@464:    =EVE='s body is composed of several rigid components that are held
rlm@464:    together by invisible joint constraints. This is what I mean by
rlm@547:    /eve-like/. The main reason that I use eve-like bodies is for
rlm@547:    simulation efficiency, and so that there will be correspondence
rlm@547:    between the AI's senses and the physical presence of its body. Each
rlm@547:    individual section is simulated by a separate rigid body that
rlm@547:    corresponds exactly with its visual representation and does not
rlm@547:    change. Sections are connected by invisible joints that are well
rlm@547:    supported in jMonkeyEngine3. Bullet, the physics backend for
rlm@547:    jMonkeyEngine3, can efficiently simulate hundreds of rigid bodies
rlm@547:    connected by joints. Just because sections are rigid does not mean
rlm@547:    they have to stay as one piece forever; they can be dynamically
rlm@547:    replaced with multiple sections to simulate splitting in two. This
rlm@547:    could be used to simulate retractable claws or =EVE='s hands, which
rlm@547:    are able to coalesce into one object in the movie.
rlm@465: 
rlm@469: *** Solidifying/Connecting a body
rlm@465: 
rlm@469:     =CORTEX= creates a creature in two steps: first, it traverses the
rlm@562:     nodes in the Blender file and creates physical representations for
rlm@562:     any of them that have mass defined in their Blender meta-data.
rlm@466: 
rlm@567:    #+caption: Program for iterating through the nodes in a Blender file
rlm@466:    #+caption: and generating physical jMonkeyEngine3 objects with mass
rlm@466:    #+caption: and a matching physics shape.
rlm@518:    #+name: physical
rlm@466:    #+begin_listing clojure
rlm@466:    #+begin_src clojure
rlm@466: (defn physical!
rlm@466:   "Iterate through the nodes in creature and make them real physical
rlm@466:    objects in the simulation."
rlm@466:   [#^Node creature]
rlm@466:   (dorun
rlm@466:    (map
rlm@466:     (fn [geom]
rlm@466:       (let [physics-control
rlm@466:             (RigidBodyControl.
rlm@466:              (HullCollisionShape.
rlm@466:               (.getMesh geom))
rlm@466:              (if-let [mass (meta-data geom "mass")]
rlm@466:                (float mass) (float 1)))]
rlm@466:         (.addControl geom physics-control)))
rlm@466:     (filter #(isa? (class %) Geometry )
rlm@466:             (node-seq creature)))))
rlm@466:    #+end_src
rlm@466:    #+end_listing
rlm@465:    
rlm@469:     The next step to making a proper body is to connect those pieces
rlm@469:     together with joints. jMonkeyEngine has a large array of joints
rlm@469:     available via =bullet=, such as Point2Point, Cone, Hinge, and a
rlm@469:     generic Six Degree of Freedom joint, with or without spring
rlm@469:     restitution. 
rlm@465: 
rlm@469:     Joints are treated a lot like proper senses, in that there is a
rlm@469:     top-level empty node named ``joints'' whose children each
rlm@469:     represent a joint.
rlm@466: 
rlm@469:     #+caption: View of the hand model in Blender showing the main ``joints''
rlm@469:     #+caption: node (highlighted in yellow) and its children which each
rlm@469:     #+caption: represent a joint in the hand. Each joint node has metadata
rlm@469:     #+caption: specifying what sort of joint it is.
rlm@469:     #+name: blender-hand
rlm@469:     #+ATTR_LaTeX: :width 10cm
rlm@469:     [[./images/hand-screenshot1.png]]
rlm@469: 
rlm@469: 
rlm@469:     =CORTEX='s procedure for binding the creature together with joints
rlm@469:     is as follows:
rlm@469:     
rlm@469:     - Find the children of the ``joints'' node.
rlm@469:     - Determine the two spatials the joint is meant to connect.
rlm@469:     - Create the joint based on the meta-data of the empty node.
rlm@469: 
rlm@469:     The higher order function =sense-nodes= from =cortex.sense=
rlm@469:     simplifies finding the joints based on their parent ``joints''
rlm@469:     node.
rlm@466: 
rlm@466:    #+caption: Retrieving the children empty nodes from a single 
rlm@563:    #+caption: named empty node is a common pattern in =CORTEX=.
rlm@563:    #+caption: Further instances of this technique for the senses 
rlm@466:    #+caption: will be omitted
rlm@466:    #+name: get-empty-nodes
rlm@466:    #+begin_listing clojure
rlm@466:    #+begin_src clojure
rlm@466: (defn sense-nodes
rlm@562:   "For some senses there is a special empty Blender node whose
rlm@466:    children are considered markers for an instance of that sense. This
rlm@466:    function generates functions to find those children, given the name
rlm@466:    of the special parent node."
rlm@466:   [parent-name]
rlm@466:   (fn [#^Node creature]
rlm@466:     (if-let [sense-node (.getChild creature parent-name)]
rlm@466:       (seq (.getChildren sense-node)) [])))
rlm@466: 
rlm@466: (def
rlm@466:   ^{:doc "Return the children of the creature's \"joints\" node."
rlm@466:     :arglists '([creature])}
rlm@466:   joints
rlm@466:   (sense-nodes "joints"))
rlm@466:    #+end_src
rlm@466:    #+end_listing
rlm@466: 
rlm@469:     To find a joint's targets, =CORTEX= creates a small cube, centered
rlm@469:     around the empty-node, and grows the cube exponentially until it
rlm@469:     intersects two physical objects. The objects are ordered according
rlm@469:     to the joint's rotation, with the first one being the object that
rlm@469:     has more negative coordinates in the joint's reference frame.
rlm@563:     Because the objects must be physical, the empty-node itself
rlm@563:     escapes detection. Because the objects must be physical,
rlm@563:     =joint-targets= must be called /after/ =physical!= is called.
rlm@464:    
rlm@469:     #+caption: Program to find the targets of a joint node by 
rlm@517:     #+caption: exponentially growth of a search cube.
rlm@469:     #+name: joint-targets
rlm@469:     #+begin_listing clojure
rlm@469:     #+begin_src clojure
rlm@466: (defn joint-targets
rlm@466:   "Return the two closest two objects to the joint object, ordered
rlm@466:   from bottom to top according to the joint's rotation."
rlm@466:   [#^Node parts #^Node joint]
rlm@466:   (loop [radius (float 0.01)]
rlm@466:     (let [results (CollisionResults.)]
rlm@466:       (.collideWith
rlm@466:        parts
rlm@466:        (BoundingBox. (.getWorldTranslation joint)
rlm@466:                      radius radius radius) results)
rlm@466:       (let [targets
rlm@466:             (distinct
rlm@466:              (map  #(.getGeometry %) results))]
rlm@466:         (if (>= (count targets) 2)
rlm@466:           (sort-by
rlm@466:            #(let [joint-ref-frame-position
rlm@466:                   (jme-to-blender
rlm@466:                    (.mult
rlm@466:                     (.inverse (.getWorldRotation joint))
rlm@466:                     (.subtract (.getWorldTranslation %)
rlm@466:                                (.getWorldTranslation joint))))]
rlm@466:               (.dot (Vector3f. 1 1 1) joint-ref-frame-position))                  
rlm@466:            (take 2 targets))
rlm@466:           (recur (float (* radius 2))))))))
rlm@469:     #+end_src
rlm@469:     #+end_listing
rlm@464:    
rlm@469:     Once =CORTEX= finds all joints and targets, it creates them using
rlm@469:     a dispatch on the metadata of each joint node.
rlm@466: 
rlm@562:     #+caption: Program to dispatch on Blender metadata and create joints
rlm@517:     #+caption: suitable for physical simulation.
rlm@469:     #+name: joint-dispatch
rlm@469:     #+begin_listing clojure
rlm@469:     #+begin_src clojure
rlm@466: (defmulti joint-dispatch
rlm@562:   "Translate Blender pseudo-joints into real JME joints."
rlm@466:   (fn [constraints & _] 
rlm@466:     (:type constraints)))
rlm@466: 
rlm@466: (defmethod joint-dispatch :point
rlm@466:   [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466:   (doto (SixDofJoint. control-a control-b pivot-a pivot-b false)
rlm@466:     (.setLinearLowerLimit Vector3f/ZERO)
rlm@466:     (.setLinearUpperLimit Vector3f/ZERO)))
rlm@466: 
rlm@466: (defmethod joint-dispatch :hinge
rlm@466:   [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466:   (let [axis (if-let [axis (:axis constraints)] axis Vector3f/UNIT_X)
rlm@466:         [limit-1 limit-2] (:limit constraints)
rlm@466:         hinge-axis (.mult rotation (blender-to-jme axis))]
rlm@466:     (doto (HingeJoint. control-a control-b pivot-a pivot-b 
rlm@466:                        hinge-axis hinge-axis)
rlm@466:       (.setLimit limit-1 limit-2))))
rlm@466: 
rlm@466: (defmethod joint-dispatch :cone
rlm@466:   [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466:   (let [limit-xz (:limit-xz constraints)
rlm@466:         limit-xy (:limit-xy constraints)
rlm@466:         twist    (:twist constraints)]
rlm@466:     (doto (ConeJoint. control-a control-b pivot-a pivot-b
rlm@466:                       rotation rotation)
rlm@466:       (.setLimit (float limit-xz) (float limit-xy)
rlm@466:                  (float twist)))))
rlm@469:     #+end_src
rlm@469:     #+end_listing
rlm@466: 
rlm@522:     All that is left for joints is to combine the above pieces into
rlm@469:     something that can operate on the collection of nodes that a
rlm@562:     Blender file represents.
rlm@466: 
rlm@469:     #+caption: Program to completely create a joint given information 
rlm@562:     #+caption: from a Blender file.
rlm@469:     #+name: connect
rlm@469:     #+begin_listing clojure
rlm@466:    #+begin_src clojure
rlm@466: (defn connect
rlm@466:   "Create a joint between 'obj-a and 'obj-b at the location of
rlm@466:   'joint. The type of joint is determined by the metadata on 'joint.
rlm@466: 
rlm@466:    Here are some examples:
rlm@466:    {:type :point}
rlm@466:    {:type :hinge  :limit [0 (/ Math/PI 2)] :axis (Vector3f. 0 1 0)}
rlm@466:    (:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)
rlm@466: 
rlm@466:    {:type :cone :limit-xz 0]
rlm@466:                 :limit-xy 0]
rlm@562:                 :twist 0]}   (use XZY rotation mode in Blender!)"
rlm@466:   [#^Node obj-a #^Node obj-b #^Node joint]
rlm@466:   (let [control-a (.getControl obj-a RigidBodyControl)
rlm@466:         control-b (.getControl obj-b RigidBodyControl)
rlm@466:         joint-center (.getWorldTranslation joint)
rlm@466:         joint-rotation (.toRotationMatrix (.getWorldRotation joint))
rlm@466:         pivot-a (world-to-local obj-a joint-center)
rlm@466:         pivot-b (world-to-local obj-b joint-center)]
rlm@466:     (if-let
rlm@466:         [constraints (map-vals eval (read-string (meta-data joint "joint")))]
rlm@466:       ;; A side-effect of creating a joint registers
rlm@466:       ;; it with both physics objects which in turn
rlm@466:       ;; will register the joint with the physics system
rlm@466:       ;; when the simulation is started.
rlm@466:         (joint-dispatch constraints
rlm@466:                         control-a control-b
rlm@466:                         pivot-a pivot-b
rlm@466:                         joint-rotation))))
rlm@469:     #+end_src
rlm@469:     #+end_listing
rlm@466: 
rlm@469:     In general, whenever =CORTEX= exposes a sense (or in this case
rlm@469:     physicality), it provides a function of the type =sense!=, which
rlm@469:     takes in a collection of nodes and augments it to support that
rlm@517:     sense. The function returns any controls necessary to use that
rlm@517:     sense. In this case =body!= creates a physical body and returns no
rlm@469:     control functions.
rlm@466: 
rlm@469:     #+caption: Program to give joints to a creature.
rlm@518:     #+name: joints
rlm@469:     #+begin_listing clojure
rlm@469:     #+begin_src clojure
rlm@466: (defn joints!
rlm@466:   "Connect the solid parts of the creature with physical joints. The
rlm@466:    joints are taken from the \"joints\" node in the creature."
rlm@466:   [#^Node creature]
rlm@466:   (dorun
rlm@466:    (map
rlm@466:     (fn [joint]
rlm@466:       (let [[obj-a obj-b] (joint-targets creature joint)]
rlm@466:         (connect obj-a obj-b joint)))
rlm@466:     (joints creature))))
rlm@466: (defn body!
rlm@466:   "Endow the creature with a physical body connected with joints.  The
rlm@466:    particulars of the joints and the masses of each body part are
rlm@562:    determined in Blender."
rlm@466:   [#^Node creature]
rlm@466:   (physical! creature)
rlm@466:   (joints! creature))
rlm@469:     #+end_src
rlm@469:     #+end_listing
rlm@466: 
rlm@469:     All of the code you have just seen amounts to only 130 lines, yet
rlm@469:     because it builds on top of Blender and jMonkeyEngine3, those few
rlm@469:     lines pack quite a punch!
rlm@466: 
rlm@469:     The hand from figure \ref{blender-hand}, which was modeled after
rlm@469:     my own right hand, can now be given joints and simulated as a
rlm@469:     creature.
rlm@466:    
rlm@562:     #+caption: With the ability to create physical creatures from Blender,
rlm@517:     #+caption: =CORTEX= gets one step closer to becoming a full creature
rlm@469:     #+caption: simulation environment.
rlm@518:     #+name: physical-hand
rlm@469:     #+ATTR_LaTeX: :width 15cm
rlm@469:     [[./images/physical-hand.png]]
rlm@468: 
rlm@511: ** Sight reuses standard video game components...
rlm@436: 
rlm@470:    Vision is one of the most important senses for humans, so I need to
rlm@470:    build a simulated sense of vision for my AI. I will do this with
rlm@470:    simulated eyes. Each eye can be independently moved and should see
rlm@470:    its own version of the world depending on where it is.
rlm@470: 
rlm@470:    Making these simulated eyes a reality is simple because
rlm@470:    jMonkeyEngine already contains extensive support for multiple views
rlm@470:    of the same 3D simulated world. The reason jMonkeyEngine has this
rlm@470:    support is because the support is necessary to create games with
rlm@470:    split-screen views. Multiple views are also used to create
rlm@470:    efficient pseudo-reflections by rendering the scene from a certain
rlm@470:    perspective and then projecting it back onto a surface in the 3D
rlm@470:    world.
rlm@470: 
rlm@470:    #+caption: jMonkeyEngine supports multiple views to enable 
rlm@470:    #+caption: split-screen games, like GoldenEye, which was one of 
rlm@470:    #+caption: the first games to use split-screen views.
rlm@518:    #+name: goldeneye
rlm@470:    #+ATTR_LaTeX: :width 10cm
rlm@470:    [[./images/goldeneye-4-player.png]]
rlm@470: 
rlm@470: *** A Brief Description of jMonkeyEngine's Rendering Pipeline
rlm@470: 
rlm@470:     jMonkeyEngine allows you to create a =ViewPort=, which represents a
rlm@470:     view of the simulated world. You can create as many of these as you
rlm@470:     want. Every frame, the =RenderManager= iterates through each
rlm@470:     =ViewPort=, rendering the scene in the GPU. For each =ViewPort= there
rlm@470:     is a =FrameBuffer= which represents the rendered image in the GPU.
rlm@470:   
rlm@470:     #+caption: =ViewPorts= are cameras in the world. During each frame, 
rlm@470:     #+caption: the =RenderManager= records a snapshot of what each view 
rlm@470:     #+caption: is currently seeing; these snapshots are =FrameBuffer= objects.
rlm@508:     #+name: rendermanagers
rlm@470:     #+ATTR_LaTeX: :width 10cm
rlm@508:     [[./images/diagram_rendermanager2.png]]
rlm@470: 
rlm@470:     Each =ViewPort= can have any number of attached =SceneProcessor=
rlm@470:     objects, which are called every time a new frame is rendered. A
rlm@470:     =SceneProcessor= receives its =ViewPort's= =FrameBuffer= and can do
rlm@470:     whatever it wants to the data.  Often this consists of invoking GPU
rlm@470:     specific operations on the rendered image.  The =SceneProcessor= can
rlm@470:     also copy the GPU image data to RAM and process it with the CPU.
rlm@470: 
rlm@470: *** Appropriating Views for Vision
rlm@470: 
rlm@470:     Each eye in the simulated creature needs its own =ViewPort= so
rlm@470:     that it can see the world from its own perspective. To this
rlm@470:     =ViewPort=, I add a =SceneProcessor= that feeds the visual data to
rlm@470:     any arbitrary continuation function for further processing. That
rlm@470:     continuation function may perform both CPU and GPU operations on
rlm@470:     the data. To make this easy for the continuation function, the
rlm@470:     =SceneProcessor= maintains appropriately sized buffers in RAM to
rlm@470:     hold the data. It does not do any copying from the GPU to the CPU
rlm@470:     itself because it is a slow operation.
rlm@470: 
rlm@517:     #+caption: Function to make the rendered scene in jMonkeyEngine 
rlm@470:     #+caption: available for further processing.
rlm@470:     #+name: pipeline-1 
rlm@470:     #+begin_listing clojure
rlm@470:     #+begin_src clojure
rlm@470: (defn vision-pipeline
rlm@470:   "Create a SceneProcessor object which wraps a vision processing
rlm@470:   continuation function. The continuation is a function that takes 
rlm@470:   [#^Renderer r #^FrameBuffer fb #^ByteBuffer b #^BufferedImage bi],
rlm@470:   each of which has already been appropriately sized."
rlm@470:   [continuation]
rlm@470:   (let [byte-buffer (atom nil)
rlm@470: 	renderer (atom nil)
rlm@470:         image (atom nil)]
rlm@470:   (proxy [SceneProcessor] []
rlm@470:     (initialize
rlm@470:      [renderManager viewPort]
rlm@470:      (let [cam (.getCamera viewPort)
rlm@470: 	   width (.getWidth cam)
rlm@470: 	   height (.getHeight cam)]
rlm@470:        (reset! renderer (.getRenderer renderManager))
rlm@470:        (reset! byte-buffer
rlm@470: 	     (BufferUtils/createByteBuffer
rlm@470: 	      (* width height 4)))
rlm@470:         (reset! image (BufferedImage.
rlm@470:                       width height
rlm@470:                       BufferedImage/TYPE_4BYTE_ABGR))))
rlm@470:     (isInitialized [] (not (nil? @byte-buffer)))
rlm@470:     (reshape [_ _ _])
rlm@470:     (preFrame [_])
rlm@470:     (postQueue [_])
rlm@470:     (postFrame
rlm@470:      [#^FrameBuffer fb]
rlm@470:      (.clear @byte-buffer)
rlm@470:      (continuation @renderer fb @byte-buffer @image))
rlm@470:     (cleanup []))))
rlm@470:     #+end_src
rlm@470:     #+end_listing
rlm@470: 
rlm@470:     The continuation function given to =vision-pipeline= above will be
rlm@470:     given a =Renderer= and three containers for image data. The
rlm@470:     =FrameBuffer= references the GPU image data, but the pixel data
rlm@470:     can not be used directly on the CPU. The =ByteBuffer= and
rlm@470:     =BufferedImage= are initially "empty" but are sized to hold the
rlm@470:     data in the =FrameBuffer=. I call transferring the GPU image data
rlm@470:     to the CPU structures "mixing" the image data.
rlm@470: 
rlm@470: *** Optical sensor arrays are described with images and referenced with metadata
rlm@470: 
rlm@470:     The vision pipeline described above handles the flow of rendered
rlm@470:     images. Now, =CORTEX= needs simulated eyes to serve as the source
rlm@470:     of these images.
rlm@470: 
rlm@562:     An eye is described in Blender in the same way as a joint. They
rlm@470:     are zero dimensional empty objects with no geometry whose local
rlm@470:     coordinate system determines the orientation of the resulting eye.
rlm@470:     All eyes are children of a parent node named "eyes" just as all
rlm@470:     joints have a parent named "joints". An eye binds to the nearest
rlm@470:     physical object with =bind-sense=.
rlm@470: 
rlm@470:     #+caption: Here, the camera is created based on metadata on the
rlm@470:     #+caption: eye-node and attached to the nearest physical object 
rlm@470:     #+caption: with =bind-sense=
rlm@470:     #+name: add-eye
rlm@470:     #+begin_listing clojure
rlm@545:     #+begin_src clojure
rlm@470: (defn add-eye!
rlm@470:   "Create a Camera centered on the current position of 'eye which
rlm@470:    follows the closest physical node in 'creature. The camera will
rlm@470:    point in the X direction and use the Z vector as up as determined
rlm@562:    by the rotation of these vectors in Blender coordinate space. Use
rlm@567:    XZY rotation for the node in Blender."
rlm@470:   [#^Node creature #^Spatial eye]
rlm@470:   (let [target (closest-node creature eye)
rlm@470:         [cam-width cam-height] 
rlm@470:         ;;[640 480] ;; graphics card on laptop doesn't support
rlm@517:                     ;; arbitrary dimensions.
rlm@470:         (eye-dimensions eye)
rlm@470:         cam (Camera. cam-width cam-height)
rlm@470:         rot (.getWorldRotation eye)]
rlm@470:     (.setLocation cam (.getWorldTranslation eye))
rlm@470:     (.lookAtDirection
rlm@470:      cam                           ; this part is not a mistake and
rlm@470:      (.mult rot Vector3f/UNIT_X)   ; is consistent with using Z in
rlm@562:      (.mult rot Vector3f/UNIT_Y))  ; Blender as the UP vector.
rlm@470:     (.setFrustumPerspective
rlm@470:      cam (float 45)
rlm@470:      (float (/ (.getWidth cam) (.getHeight cam)))
rlm@470:      (float 1)
rlm@470:      (float 1000))
rlm@470:     (bind-sense target cam) cam))
rlm@545:     #+end_src
rlm@470:     #+end_listing
rlm@470: 
rlm@470: *** Simulated Retina 
rlm@470: 
rlm@470:     An eye is a surface (the retina) which contains many discrete
rlm@470:     sensors to detect light. These sensors can have different
rlm@470:     light-sensing properties. In humans, each discrete sensor is
rlm@470:     sensitive to red, blue, green, or gray. These different types of
rlm@470:     sensors can have different spatial distributions along the retina.
rlm@470:     In humans, there is a fovea in the center of the retina which has
rlm@470:     a very high density of color sensors, and a blind spot which has
rlm@470:     no sensors at all. Sensor density decreases in proportion to
rlm@470:     distance from the fovea.
rlm@470: 
rlm@470:     I want to be able to model any retinal configuration, so my
rlm@562:     eye-nodes in Blender contain metadata pointing to images that
rlm@470:     describe the precise position of the individual sensors using
rlm@470:     white pixels. The meta-data also describes the precise sensitivity
rlm@470:     to light that the sensors described in the image have. An eye can
rlm@470:     contain any number of these images. For example, the metadata for
rlm@470:     an eye might look like this:
rlm@470: 
rlm@470:     #+begin_src clojure
rlm@470: {0xFF0000 "Models/test-creature/retina-small.png"}
rlm@470:     #+end_src
rlm@470: 
rlm@470:     #+caption: An example retinal profile image. White pixels are 
rlm@470:     #+caption: photo-sensitive elements. The distribution of white 
rlm@470:     #+caption: pixels is denser in the middle and falls off at the 
rlm@470:     #+caption: edges and is inspired by the human retina.
rlm@470:     #+name: retina
rlm@510:     #+ATTR_LaTeX: :width 7cm
rlm@470:     [[./images/retina-small.png]]
rlm@470: 
rlm@545:     Together, the number 0xFF0000 and the image above describe the
rlm@545:     placement of red-sensitive sensory elements.
rlm@470: 
rlm@470:     Meta-data to very crudely approximate a human eye might be
rlm@470:     something like this:
rlm@470: 
rlm@470:     #+begin_src clojure
rlm@470: (let [retinal-profile "Models/test-creature/retina-small.png"]
rlm@470:   {0xFF0000 retinal-profile
rlm@470:    0x00FF00 retinal-profile
rlm@470:    0x0000FF retinal-profile
rlm@470:    0xFFFFFF retinal-profile})
rlm@470:     #+end_src
rlm@470: 
rlm@470:     The numbers that serve as keys in the map determine a sensor's
rlm@470:     relative sensitivity to the channels red, green, and blue. These
rlm@470:     sensitivity values are packed into an integer in the order
rlm@470:     =|_|R|G|B|= in 8-bit fields. The RGB values of a pixel in the
rlm@470:     image are added together with these sensitivities as linear
rlm@470:     weights. Therefore, 0xFF0000 means sensitive to red only while
rlm@470:     0xFFFFFF means sensitive to all colors equally (gray).
rlm@470: 
rlm@470:     #+caption: This is the core of vision in =CORTEX=. A given eye node 
rlm@470:     #+caption: is converted into a function that returns visual
rlm@470:     #+caption: information from the simulation.
rlm@471:     #+name: vision-kernel
rlm@470:     #+begin_listing clojure
rlm@508:     #+BEGIN_SRC clojure
rlm@470: (defn vision-kernel
rlm@470:   "Returns a list of functions, each of which will return a color
rlm@470:    channel's worth of visual information when called inside a running
rlm@470:    simulation."
rlm@470:   [#^Node creature #^Spatial eye & {skip :skip :or {skip 0}}]
rlm@470:   (let [retinal-map (retina-sensor-profile eye)
rlm@470:         camera (add-eye! creature eye)
rlm@470:         vision-image
rlm@470:         (atom
rlm@470:          (BufferedImage. (.getWidth camera)
rlm@470:                          (.getHeight camera)
rlm@470:                          BufferedImage/TYPE_BYTE_BINARY))
rlm@470:         register-eye!
rlm@470:         (runonce
rlm@470:          (fn [world]
rlm@470:            (add-camera!
rlm@470:             world camera
rlm@470:             (let [counter  (atom 0)]
rlm@470:               (fn [r fb bb bi]
rlm@470:                 (if (zero? (rem (swap! counter inc) (inc skip)))
rlm@470:                   (reset! vision-image
rlm@470:                           (BufferedImage! r fb bb bi))))))))]
rlm@470:      (vec
rlm@470:       (map
rlm@470:        (fn [[key image]]
rlm@470:          (let [whites (white-coordinates image)
rlm@470:                topology (vec (collapse whites))
rlm@470:                sensitivity (sensitivity-presets key key)]
rlm@470:            (attached-viewport.
rlm@470:             (fn [world]
rlm@470:               (register-eye! world)
rlm@470:               (vector
rlm@470:                topology
rlm@470:                (vec 
rlm@470:                 (for [[x y] whites]
rlm@470:                   (pixel-sense 
rlm@470:                    sensitivity
rlm@470:                    (.getRGB @vision-image x y))))))
rlm@470:             register-eye!)))
rlm@470:          retinal-map))))
rlm@508:     #+END_SRC
rlm@470:     #+end_listing
rlm@470: 
rlm@563:     Note that because each of the functions generated by
rlm@563:     =vision-kernel= shares the same =register-eye!= function, the eye
rlm@563:     will be registered only once the first time any of the functions
rlm@563:     from the list returned by =vision-kernel= is called. Each of the
rlm@563:     functions returned by =vision-kernel= also allows access to the
rlm@563:     =Viewport= through which it receives images.
rlm@470: 
rlm@470:     All the hard work has been done; all that remains is to apply
rlm@470:     =vision-kernel= to each eye in the creature and gather the results
rlm@470:     into one list of functions.
rlm@470: 
rlm@470: 
rlm@470:     #+caption: With =vision!=, =CORTEX= is already a fine simulation 
rlm@470:     #+caption: environment for experimenting with different types of 
rlm@470:     #+caption: eyes.
rlm@470:     #+name: vision!
rlm@470:     #+begin_listing clojure
rlm@508:     #+BEGIN_SRC clojure
rlm@470: (defn vision!
rlm@470:   "Returns a list of functions, each of which returns visual sensory
rlm@470:    data when called inside a running simulation."
rlm@470:   [#^Node creature & {skip :skip :or {skip 0}}]
rlm@470:   (reduce
rlm@470:    concat 
rlm@470:    (for [eye (eyes creature)]
rlm@470:      (vision-kernel creature eye))))
rlm@508:     #+END_SRC
rlm@470:     #+end_listing
rlm@470: 
rlm@471:     #+caption: Simulated vision with a test creature and the 
rlm@471:     #+caption: human-like eye approximation. Notice how each channel
rlm@471:     #+caption: of the eye responds differently to the differently 
rlm@471:     #+caption: colored balls.
rlm@471:     #+name: worm-vision-test.
rlm@471:     #+ATTR_LaTeX: :width 13cm
rlm@471:     [[./images/worm-vision.png]]
rlm@470: 
rlm@471:     The vision code is not much more complicated than the body code,
rlm@471:     and enables multiple further paths for simulated vision. For
rlm@471:     example, it is quite easy to create bifocal vision -- you just
rlm@567:     make two eyes next to each other in Blender! It is also possible
rlm@471:     to encode vision transforms in the retinal files. For example, the
rlm@471:     human like retina file in figure \ref{retina} approximates a
rlm@471:     log-polar transform.
rlm@470: 
rlm@471:     This vision code has already been absorbed by the jMonkeyEngine
rlm@471:     community and is now (in modified form) part of a system for
rlm@471:     capturing in-game video to a file.
rlm@470: 
rlm@511: ** ...but hearing must be built from scratch
rlm@514: 
rlm@551:    At the end of this chapter I will have simulated ears that work the
rlm@551:    same way as the simulated eyes in the last chapter. I will be able to
rlm@562:    place any number of ear-nodes in a Blender file, and they will bind to
rlm@472:    the closest physical object and follow it as it moves around. Each ear
rlm@472:    will provide access to the sound data it picks up between every frame.
rlm@472: 
rlm@472:    Hearing is one of the more difficult senses to simulate, because there
rlm@472:    is less support for obtaining the actual sound data that is processed
rlm@472:    by jMonkeyEngine3. There is no "split-screen" support for rendering
rlm@472:    sound from different points of view, and there is no way to directly
rlm@472:    access the rendered sound data.
rlm@472: 
rlm@472:    =CORTEX='s hearing is unique because it does not have any
rlm@472:    limitations compared to other simulation environments. As far as I
rlm@517:    know, there is no other system that supports multiple listeners,
rlm@551:    and the sound demo at the end of this chapter is the first time
rlm@472:    it's been done in a video game environment.
rlm@472: 
rlm@472: *** Brief Description of jMonkeyEngine's Sound System
rlm@472: 
rlm@472:    jMonkeyEngine's sound system works as follows:
rlm@472: 
rlm@472:    - jMonkeyEngine uses the =AppSettings= for the particular
rlm@472:      application to determine what sort of =AudioRenderer= should be
rlm@472:      used.
rlm@556:    - Although some support is provided for multiple AudioRenderer
rlm@472:      backends, jMonkeyEngine at the time of this writing will either
rlm@472:      pick no =AudioRenderer= at all, or the =LwjglAudioRenderer=.
rlm@472:    - jMonkeyEngine tries to figure out what sort of system you're
rlm@472:      running and extracts the appropriate native libraries.
rlm@472:    - The =LwjglAudioRenderer= uses the [[http://lwjgl.org/][=LWJGL=]] (LightWeight Java Game
rlm@472:      Library) bindings to interface with a C library called [[http://kcat.strangesoft.net/openal.html][=OpenAL=]]
rlm@472:    - =OpenAL= renders the 3D sound and feeds the rendered sound
rlm@472:      directly to any of various sound output devices with which it
rlm@472:      knows how to communicate.
rlm@472:   
rlm@472:    A consequence of this is that there's no way to access the actual
rlm@472:    sound data produced by =OpenAL=. Even worse, =OpenAL= only supports
rlm@472:    one /listener/ (it renders sound data from only one perspective),
rlm@472:    which normally isn't a problem for games, but becomes a problem
rlm@472:    when trying to make multiple AI creatures that can each hear the
rlm@472:    world from a different perspective.
rlm@472: 
rlm@472:    To make many AI creatures in jMonkeyEngine that can each hear the
rlm@472:    world from their own perspective, or to make a single creature with
rlm@472:    many ears, it is necessary to go all the way back to =OpenAL= and
rlm@472:    implement support for simulated hearing there.
rlm@472: 
rlm@472: *** Extending =OpenAl=
rlm@472: 
rlm@472:     Extending =OpenAL= to support multiple listeners requires 500
rlm@563:     lines of =C= code and is too complicated to mention here. Instead,
rlm@563:     I will show a small amount of extension code and go over the high
rlm@517:     level strategy. Full source is of course available with the
rlm@472:     =CORTEX= distribution if you're interested.
rlm@472: 
rlm@472:     =OpenAL= goes to great lengths to support many different systems,
rlm@472:     all with different sound capabilities and interfaces. It
rlm@472:     accomplishes this difficult task by providing code for many
rlm@472:     different sound backends in pseudo-objects called /Devices/.
rlm@472:     There's a device for the Linux Open Sound System and the Advanced
rlm@472:     Linux Sound Architecture, there's one for Direct Sound on Windows,
rlm@472:     and there's even one for Solaris. =OpenAL= solves the problem of
rlm@472:     platform independence by providing all these Devices.
rlm@472: 
rlm@472:     Wrapper libraries such as LWJGL are free to examine the system on
rlm@472:     which they are running and then select an appropriate device for
rlm@472:     that system.
rlm@472: 
rlm@472:     There are also a few "special" devices that don't interface with
rlm@472:     any particular system. These include the Null Device, which
rlm@472:     doesn't do anything, and the Wave Device, which writes whatever
rlm@472:     sound it receives to a file, if everything has been set up
rlm@472:     correctly when configuring =OpenAL=.
rlm@472: 
rlm@517:     Actual mixing (Doppler shift and distance.environment-based
rlm@472:     attenuation) of the sound data happens in the Devices, and they
rlm@472:     are the only point in the sound rendering process where this data
rlm@472:     is available.
rlm@472: 
rlm@472:     Therefore, in order to support multiple listeners, and get the
rlm@472:     sound data in a form that the AIs can use, it is necessary to
rlm@472:     create a new Device which supports this feature.
rlm@472: 
rlm@472:     Adding a device to OpenAL is rather tricky -- there are five
rlm@472:     separate files in the =OpenAL= source tree that must be modified
rlm@472:     to do so. I named my device the "Multiple Audio Send" Device, or
rlm@472:     =Send= Device for short, since it sends audio data back to the
rlm@472:     calling application like an Aux-Send cable on a mixing board.
rlm@472: 
rlm@472:     The main idea behind the Send device is to take advantage of the
rlm@472:     fact that LWJGL only manages one /context/ when using OpenAL. A
rlm@472:     /context/ is like a container that holds samples and keeps track
rlm@472:     of where the listener is. In order to support multiple listeners,
rlm@472:     the Send device identifies the LWJGL context as the master
rlm@472:     context, and creates any number of slave contexts to represent
rlm@472:     additional listeners. Every time the device renders sound, it
rlm@472:     synchronizes every source from the master LWJGL context to the
rlm@472:     slave contexts. Then, it renders each context separately, using a
rlm@472:     different listener for each one. The rendered sound is made
rlm@472:     available via JNI to jMonkeyEngine.
rlm@472: 
rlm@472:     Switching between contexts is not the normal operation of a
rlm@472:     Device, and one of the problems with doing so is that a Device
rlm@472:     normally keeps around a few pieces of state such as the
rlm@472:     =ClickRemoval= array above which will become corrupted if the
rlm@472:     contexts are not rendered in parallel. The solution is to create a
rlm@472:     copy of this normally global device state for each context, and
rlm@472:     copy it back and forth into and out of the actual device state
rlm@472:     whenever a context is rendered.
rlm@472: 
rlm@472:     The core of the =Send= device is the =syncSources= function, which
rlm@472:     does the job of copying all relevant data from one context to
rlm@472:     another. 
rlm@472: 
rlm@472:     #+caption: Program for extending =OpenAL= to support multiple
rlm@472:     #+caption: listeners via context copying/switching.
rlm@472:     #+name: sync-openal-sources
rlm@509:     #+begin_listing c
rlm@509:     #+BEGIN_SRC c
rlm@472: void syncSources(ALsource *masterSource, ALsource *slaveSource, 
rlm@472: 		 ALCcontext *masterCtx, ALCcontext *slaveCtx){
rlm@472:   ALuint master = masterSource->source;
rlm@472:   ALuint slave = slaveSource->source;
rlm@472:   ALCcontext *current = alcGetCurrentContext();
rlm@472: 
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_PITCH);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_GAIN);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_DISTANCE);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_ROLLOFF_FACTOR);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_REFERENCE_DISTANCE);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MIN_GAIN);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_GAIN);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_GAIN);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_INNER_ANGLE);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_ANGLE);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_SEC_OFFSET);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_SAMPLE_OFFSET);
rlm@472:   syncSourcef(master,slave,masterCtx,slaveCtx,AL_BYTE_OFFSET);
rlm@472:     
rlm@472:   syncSource3f(master,slave,masterCtx,slaveCtx,AL_POSITION);
rlm@472:   syncSource3f(master,slave,masterCtx,slaveCtx,AL_VELOCITY);
rlm@472:   syncSource3f(master,slave,masterCtx,slaveCtx,AL_DIRECTION);
rlm@472:   
rlm@472:   syncSourcei(master,slave,masterCtx,slaveCtx,AL_SOURCE_RELATIVE);
rlm@472:   syncSourcei(master,slave,masterCtx,slaveCtx,AL_LOOPING);
rlm@472: 
rlm@472:   alcMakeContextCurrent(masterCtx);
rlm@472:   ALint source_type;
rlm@472:   alGetSourcei(master, AL_SOURCE_TYPE, &source_type);
rlm@472: 
rlm@472:   // Only static sources are currently synchronized! 
rlm@472:   if (AL_STATIC == source_type){
rlm@472:     ALint master_buffer;
rlm@472:     ALint slave_buffer;
rlm@472:     alGetSourcei(master, AL_BUFFER, &master_buffer);
rlm@472:     alcMakeContextCurrent(slaveCtx);
rlm@472:     alGetSourcei(slave, AL_BUFFER, &slave_buffer);
rlm@472:     if (master_buffer != slave_buffer){
rlm@472:       alSourcei(slave, AL_BUFFER, master_buffer);
rlm@472:     }
rlm@472:   }
rlm@472:   
rlm@472:   // Synchronize the state of the two sources.
rlm@472:   alcMakeContextCurrent(masterCtx);
rlm@472:   ALint masterState;
rlm@472:   ALint slaveState;
rlm@472: 
rlm@472:   alGetSourcei(master, AL_SOURCE_STATE, &masterState);
rlm@472:   alcMakeContextCurrent(slaveCtx);
rlm@472:   alGetSourcei(slave, AL_SOURCE_STATE, &slaveState);
rlm@472: 
rlm@472:   if (masterState != slaveState){
rlm@472:     switch (masterState){
rlm@472:     case AL_INITIAL : alSourceRewind(slave); break;
rlm@472:     case AL_PLAYING : alSourcePlay(slave);   break;
rlm@472:     case AL_PAUSED  : alSourcePause(slave);  break;
rlm@472:     case AL_STOPPED : alSourceStop(slave);   break;
rlm@472:     }
rlm@472:   }
rlm@472:   // Restore whatever context was previously active.
rlm@472:   alcMakeContextCurrent(current);
rlm@472: }
rlm@508:     #+END_SRC
rlm@472:     #+end_listing
rlm@472: 
rlm@472:     With this special context-switching device, and some ugly JNI
rlm@472:     bindings that are not worth mentioning, =CORTEX= gains the ability
rlm@472:     to access multiple sound streams from =OpenAL=. 
rlm@472: 
rlm@567:     #+caption: Program to create an ear from a Blender empty node. The ear
rlm@472:     #+caption: follows around the nearest physical object and passes 
rlm@472:     #+caption: all sensory data to a continuation function.
rlm@472:     #+name: add-ear
rlm@472:     #+begin_listing clojure
rlm@508:     #+BEGIN_SRC clojure
rlm@472: (defn add-ear!  
rlm@472:   "Create a Listener centered on the current position of 'ear 
rlm@472:    which follows the closest physical node in 'creature and 
rlm@472:    sends sound data to 'continuation."
rlm@472:   [#^Application world #^Node creature #^Spatial ear continuation]
rlm@472:   (let [target (closest-node creature ear)
rlm@472:         lis (Listener.)
rlm@472:         audio-renderer (.getAudioRenderer world)
rlm@472:         sp (hearing-pipeline continuation)]
rlm@472:     (.setLocation lis (.getWorldTranslation ear))
rlm@472:     (.setRotation lis (.getWorldRotation ear))
rlm@472:     (bind-sense target lis)
rlm@472:     (update-listener-velocity! target lis)
rlm@472:     (.addListener audio-renderer lis)
rlm@472:     (.registerSoundProcessor audio-renderer lis sp)))
rlm@508:     #+END_SRC
rlm@472:     #+end_listing
rlm@472:     
rlm@472:     The =Send= device, unlike most of the other devices in =OpenAL=,
rlm@472:     does not render sound unless asked. This enables the system to
rlm@472:     slow down or speed up depending on the needs of the AIs who are
rlm@472:     using it to listen. If the device tried to render samples in
rlm@472:     real-time, a complicated AI whose mind takes 100 seconds of
rlm@472:     computer time to simulate 1 second of AI-time would miss almost
rlm@472:     all of the sound in its environment!
rlm@472: 
rlm@472:     #+caption: Program to enable arbitrary hearing in =CORTEX=
rlm@472:     #+name: hearing
rlm@472:     #+begin_listing clojure
rlm@508: #+BEGIN_SRC clojure
rlm@472: (defn hearing-kernel
rlm@472:   "Returns a function which returns auditory sensory data when called
rlm@472:    inside a running simulation."
rlm@472:   [#^Node creature #^Spatial ear]
rlm@472:   (let [hearing-data (atom [])
rlm@472:         register-listener!
rlm@472:         (runonce 
rlm@472:          (fn [#^Application world]
rlm@472:            (add-ear!
rlm@472:             world creature ear
rlm@472:             (comp #(reset! hearing-data %)
rlm@472:                   byteBuffer->pulse-vector))))]
rlm@472:     (fn [#^Application world]
rlm@472:       (register-listener! world)
rlm@472:       (let [data @hearing-data
rlm@472:             topology              
rlm@472:             (vec (map #(vector % 0) (range 0 (count data))))]
rlm@472:         [topology data]))))
rlm@472:     
rlm@472: (defn hearing!
rlm@472:   "Endow the creature in a particular world with the sense of
rlm@472:    hearing. Will return a sequence of functions, one for each ear,
rlm@472:    which when called will return the auditory data from that ear."
rlm@472:   [#^Node creature]
rlm@472:   (for [ear (ears creature)]
rlm@472:     (hearing-kernel creature ear)))
rlm@508:     #+END_SRC
rlm@472:     #+end_listing
rlm@472: 
rlm@472:     Armed with these functions, =CORTEX= is able to test possibly the
rlm@472:     first ever instance of multiple listeners in a video game engine
rlm@472:     based simulation!
rlm@472: 
rlm@472:     #+caption: Here a simple creature responds to sound by changing
rlm@472:     #+caption: its color from gray to green when the total volume
rlm@472:     #+caption: goes over a threshold.
rlm@472:     #+name: sound-test
rlm@472:     #+begin_listing java
rlm@508:     #+BEGIN_SRC java
rlm@472: /**
rlm@472:  * Respond to sound!  This is the brain of an AI entity that 
rlm@472:  * hears its surroundings and reacts to them.
rlm@472:  */
rlm@472: public void process(ByteBuffer audioSamples, 
rlm@472: 		    int numSamples, AudioFormat format) {
rlm@472:     audioSamples.clear();
rlm@472:     byte[] data = new byte[numSamples];
rlm@472:     float[] out = new float[numSamples];
rlm@472:     audioSamples.get(data);
rlm@472:     FloatSampleTools.
rlm@472: 	byte2floatInterleaved
rlm@472: 	(data, 0, out, 0, numSamples/format.getFrameSize(), format);
rlm@472: 
rlm@472:     float max = Float.NEGATIVE_INFINITY;
rlm@472:     for (float f : out){if (f > max) max = f;}
rlm@472:     audioSamples.clear();
rlm@472: 
rlm@472:     if (max > 0.1){
rlm@472: 	entity.getMaterial().setColor("Color", ColorRGBA.Green);
rlm@472:     }
rlm@472:     else {
rlm@472: 	entity.getMaterial().setColor("Color", ColorRGBA.Gray);
rlm@472:     }
rlm@508:     #+END_SRC
rlm@472:     #+end_listing
rlm@472: 
rlm@517:     #+caption: First ever simulation of multiple listeners in =CORTEX=.
rlm@472:     #+caption: Each cube is a creature which processes sound data with
rlm@472:     #+caption: the =process= function from listing \ref{sound-test}. 
rlm@517:     #+caption: the ball is constantly emitting a pure tone of
rlm@472:     #+caption: constant volume. As it approaches the cubes, they each
rlm@472:     #+caption: change color in response to the sound.
rlm@472:     #+name: sound-cubes.
rlm@472:     #+ATTR_LaTeX: :width 10cm
rlm@509:     [[./images/java-hearing-test.png]]
rlm@472: 
rlm@472:     This system of hearing has also been co-opted by the
rlm@472:     jMonkeyEngine3 community and is used to record audio for demo
rlm@472:     videos.
rlm@472: 
rlm@511: ** Hundreds of hair-like elements provide a sense of touch
rlm@436: 
rlm@474:    Touch is critical to navigation and spatial reasoning and as such I
rlm@474:    need a simulated version of it to give to my AI creatures.
rlm@474:    
rlm@474:    Human skin has a wide array of touch sensors, each of which
rlm@474:    specialize in detecting different vibrational modes and pressures.
rlm@474:    These sensors can integrate a vast expanse of skin (i.e. your
rlm@474:    entire palm), or a tiny patch of skin at the tip of your finger.
rlm@474:    The hairs of the skin help detect objects before they even come
rlm@474:    into contact with the skin proper.
rlm@474:    
rlm@474:    However, touch in my simulated world can not exactly correspond to
rlm@474:    human touch because my creatures are made out of completely rigid
rlm@474:    segments that don't deform like human skin.
rlm@474:    
rlm@474:    Instead of measuring deformation or vibration, I surround each
rlm@474:    rigid part with a plenitude of hair-like objects (/feelers/) which
rlm@474:    do not interact with the physical world. Physical objects can pass
rlm@474:    through them with no effect. The feelers are able to tell when
rlm@474:    other objects pass through them, and they constantly report how
rlm@474:    much of their extent is covered. So even though the creature's body
rlm@474:    parts do not deform, the feelers create a margin around those body
rlm@474:    parts which achieves a sense of touch which is a hybrid between a
rlm@474:    human's sense of deformation and sense from hairs.
rlm@474:    
rlm@474:    Implementing touch in jMonkeyEngine follows a different technical
rlm@474:    route than vision and hearing. Those two senses piggybacked off
rlm@474:    jMonkeyEngine's 3D audio and video rendering subsystems. To
rlm@474:    simulate touch, I use jMonkeyEngine's physics system to execute
rlm@474:    many small collision detections, one for each feeler. The placement
rlm@474:    of the feelers is determined by a UV-mapped image which shows where
rlm@474:    each feeler should be on the 3D surface of the body.
rlm@474: 
rlm@477: *** Defining Touch Meta-Data in Blender
rlm@474: 
rlm@474:     Each geometry can have a single UV map which describes the
rlm@474:     position of the feelers which will constitute its sense of touch.
rlm@474:     This image path is stored under the ``touch'' key. The image itself
rlm@474:     is black and white, with black meaning a feeler length of 0 (no
rlm@474:     feeler is present) and white meaning a feeler length of =scale=,
rlm@474:     which is a float stored under the key "scale".
rlm@474: 
rlm@475:     #+caption: Touch does not use empty nodes, to store metadata, 
rlm@475:     #+caption: because the metadata of each solid part of a 
rlm@475:     #+caption: creature's body is sufficient.
rlm@475:     #+name: touch-meta-data
rlm@475:     #+begin_listing clojure
rlm@477:     #+BEGIN_SRC  clojure
rlm@474: (defn tactile-sensor-profile
rlm@474:   "Return the touch-sensor distribution image in BufferedImage format,
rlm@474:    or nil if it does not exist."
rlm@474:   [#^Geometry obj]
rlm@474:   (if-let [image-path (meta-data obj "touch")]
rlm@474:     (load-image image-path)))
rlm@474: 
rlm@474: (defn tactile-scale
rlm@474:   "Return the length of each feeler. Default scale is 0.01
rlm@474:   jMonkeyEngine units."
rlm@474:   [#^Geometry obj]
rlm@474:   (if-let [scale (meta-data obj "scale")]
rlm@474:     scale 0.1))
rlm@477:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@475:     Here is an example of a UV-map which specifies the position of
rlm@475:     touch sensors along the surface of the upper segment of a fingertip.
rlm@474: 
rlm@475:     #+caption: This is the tactile-sensor-profile for the upper segment 
rlm@475:     #+caption: of a fingertip. It defines regions of high touch sensitivity 
rlm@475:     #+caption: (where there are many white pixels) and regions of low 
rlm@475:     #+caption: sensitivity (where white pixels are sparse).
rlm@486:     #+name: fingertip-UV
rlm@477:     #+ATTR_LaTeX: :width 13cm
rlm@477:     [[./images/finger-UV.png]]
rlm@474: 
rlm@477: *** Implementation Summary
rlm@474:   
rlm@474:     To simulate touch there are three conceptual steps. For each solid
rlm@474:     object in the creature, you first have to get UV image and scale
rlm@474:     parameter which define the position and length of the feelers.
rlm@474:     Then, you use the triangles which comprise the mesh and the UV
rlm@474:     data stored in the mesh to determine the world-space position and
rlm@474:     orientation of each feeler. Then once every frame, update these
rlm@474:     positions and orientations to match the current position and
rlm@474:     orientation of the object, and use physics collision detection to
rlm@474:     gather tactile data.
rlm@474:     
rlm@474:     Extracting the meta-data has already been described. The third
rlm@474:     step, physics collision detection, is handled in =touch-kernel=.
rlm@474:     Translating the positions and orientations of the feelers from the
rlm@474:     UV-map to world-space is itself a three-step process.
rlm@474: 
rlm@475:     - Find the triangles which make up the mesh in pixel-space and in
rlm@505:       world-space. \\(=triangles=, =pixel-triangles=).
rlm@474: 
rlm@475:     - Find the coordinates of each feeler in world-space. These are
rlm@475:       the origins of the feelers. (=feeler-origins=).
rlm@474:     
rlm@475:     - Calculate the normals of the triangles in world space, and add
rlm@475:       them to each of the origins of the feelers. These are the
rlm@475:       normalized coordinates of the tips of the feelers.
rlm@475:       (=feeler-tips=).
rlm@474: 
rlm@477: *** Triangle Math
rlm@474: 
rlm@475:     The rigid objects which make up a creature have an underlying
rlm@475:     =Geometry=, which is a =Mesh= plus a =Material= and other
rlm@475:     important data involved with displaying the object.
rlm@475:     
rlm@475:     A =Mesh= is composed of =Triangles=, and each =Triangle= has three
rlm@475:     vertices which have coordinates in world space and UV space.
rlm@475:     
rlm@475:     Here, =triangles= gets all the world-space triangles which
rlm@475:     comprise a mesh, while =pixel-triangles= gets those same triangles
rlm@475:     expressed in pixel coordinates (which are UV coordinates scaled to
rlm@475:     fit the height and width of the UV image).
rlm@474: 
rlm@475:     #+caption: Programs to extract triangles from a geometry and get 
rlm@517:     #+caption: their vertices in both world and UV-coordinates.
rlm@475:     #+name: get-triangles
rlm@475:     #+begin_listing clojure
rlm@477:     #+BEGIN_SRC clojure
rlm@474: (defn triangle
rlm@474:   "Get the triangle specified by triangle-index from the mesh."
rlm@474:   [#^Geometry geo triangle-index]
rlm@474:   (triangle-seq
rlm@474:    (let [scratch (Triangle.)]
rlm@474:      (.getTriangle (.getMesh geo) triangle-index scratch) scratch)))
rlm@474: 
rlm@474: (defn triangles
rlm@474:   "Return a sequence of all the Triangles which comprise a given
rlm@474:    Geometry." 
rlm@474:   [#^Geometry geo]
rlm@474:   (map (partial triangle geo) (range (.getTriangleCount (.getMesh geo)))))
rlm@474: 
rlm@474: (defn triangle-vertex-indices
rlm@474:   "Get the triangle vertex indices of a given triangle from a given
rlm@474:    mesh."
rlm@474:   [#^Mesh mesh triangle-index]
rlm@474:   (let [indices (int-array 3)]
rlm@474:     (.getTriangle mesh triangle-index indices)
rlm@474:     (vec indices)))
rlm@474: 
rlm@475:     (defn vertex-UV-coord
rlm@474:   "Get the UV-coordinates of the vertex named by vertex-index"
rlm@474:   [#^Mesh mesh vertex-index]
rlm@474:   (let [UV-buffer
rlm@474:         (.getData
rlm@474:          (.getBuffer
rlm@474:           mesh
rlm@474:           VertexBuffer$Type/TexCoord))]
rlm@474:     [(.get UV-buffer (* vertex-index 2))
rlm@474:      (.get UV-buffer (+ 1 (* vertex-index 2)))]))
rlm@474: 
rlm@474: (defn pixel-triangle [#^Geometry geo image index]
rlm@474:   (let [mesh (.getMesh geo)
rlm@474:         width (.getWidth image)
rlm@474:         height (.getHeight image)]
rlm@474:     (vec (map (fn [[u v]] (vector (* width u) (* height v)))
rlm@474:               (map (partial vertex-UV-coord mesh)
rlm@474:                    (triangle-vertex-indices mesh index))))))
rlm@474: 
rlm@474: (defn pixel-triangles 
rlm@474:   "The pixel-space triangles of the Geometry, in the same order as
rlm@474:    (triangles geo)"
rlm@474:   [#^Geometry geo image]
rlm@474:   (let [height (.getHeight image)
rlm@474:         width (.getWidth image)]
rlm@474:     (map (partial pixel-triangle geo image)
rlm@474:          (range (.getTriangleCount (.getMesh geo))))))
rlm@477:     #+END_SRC
rlm@475:     #+end_listing
rlm@475:     
rlm@474: *** The Affine Transform from one Triangle to Another
rlm@474: 
rlm@475:     =pixel-triangles= gives us the mesh triangles expressed in pixel
rlm@475:     coordinates and =triangles= gives us the mesh triangles expressed
rlm@475:     in world coordinates. The tactile-sensor-profile gives the
rlm@475:     position of each feeler in pixel-space. In order to convert
rlm@475:     pixel-space coordinates into world-space coordinates we need
rlm@475:     something that takes coordinates on the surface of one triangle
rlm@475:     and gives the corresponding coordinates on the surface of another
rlm@475:     triangle.
rlm@475:     
rlm@475:     Triangles are [[http://mathworld.wolfram.com/AffineTransformation.html ][affine]], which means any triangle can be transformed
rlm@475:     into any other by a combination of translation, scaling, and
rlm@475:     rotation. The affine transformation from one triangle to another
rlm@475:     is readily computable if the triangle is expressed in terms of a
rlm@475:     $4x4$ matrix.
rlm@476: 
rlm@476:     #+BEGIN_LaTeX
rlm@476:     $$
rlm@475:     \begin{bmatrix}
rlm@475:     x_1 & x_2 & x_3 & n_x \\
rlm@475:     y_1 & y_2 & y_3 & n_y \\ 
rlm@475:     z_1 & z_2 & z_3 & n_z \\
rlm@475:     1 & 1 & 1 & 1 
rlm@475:     \end{bmatrix}
rlm@476:     $$
rlm@476:     #+END_LaTeX
rlm@475:     
rlm@475:     Here, the first three columns of the matrix are the vertices of
rlm@475:     the triangle. The last column is the right-handed unit normal of
rlm@475:     the triangle.
rlm@475:     
rlm@476:     With two triangles $T_{1}$ and $T_{2}$ each expressed as a
rlm@476:     matrix like above, the affine transform from $T_{1}$ to $T_{2}$
rlm@476:     is $T_{2}T_{1}^{-1}$.
rlm@475:     
rlm@475:     The clojure code below recapitulates the formulas above, using
rlm@475:     jMonkeyEngine's =Matrix4f= objects, which can describe any affine
rlm@475:     transformation.
rlm@474: 
rlm@517:     #+caption: Program to interpret triangles as affine transforms.
rlm@475:     #+name: triangle-affine
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn triangle->matrix4f
rlm@474:   "Converts the triangle into a 4x4 matrix: The first three columns
rlm@474:    contain the vertices of the triangle; the last contains the unit
rlm@474:    normal of the triangle. The bottom row is filled with 1s."
rlm@474:   [#^Triangle t]
rlm@474:   (let [mat (Matrix4f.)
rlm@474:         [vert-1 vert-2 vert-3]
rlm@474:         (mapv #(.get t %) (range 3))
rlm@474:         unit-normal (do (.calculateNormal t)(.getNormal t))
rlm@474:         vertices [vert-1 vert-2 vert-3 unit-normal]]
rlm@474:     (dorun 
rlm@474:      (for [row (range 4) col (range 3)]
rlm@474:        (do
rlm@474:          (.set mat col row (.get (vertices row) col))
rlm@474:          (.set mat 3 row 1)))) mat))
rlm@474: 
rlm@474: (defn triangles->affine-transform
rlm@474:   "Returns the affine transformation that converts each vertex in the
rlm@474:    first triangle into the corresponding vertex in the second
rlm@474:    triangle."
rlm@474:   [#^Triangle tri-1 #^Triangle tri-2]
rlm@474:   (.mult 
rlm@474:    (triangle->matrix4f tri-2)
rlm@474:    (.invert (triangle->matrix4f tri-1))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@477: *** Triangle Boundaries
rlm@474:   
rlm@474: For efficiency's sake I will divide the tactile-profile image into
rlm@474: small squares which inscribe each pixel-triangle, then extract the
rlm@474: points which lie inside the triangle and map them to 3D-space using
rlm@474: =triangle-transform= above. To do this I need a function,
rlm@474: =convex-bounds= which finds the smallest box which inscribes a 2D
rlm@474: triangle.
rlm@474: 
rlm@474: =inside-triangle?= determines whether a point is inside a triangle
rlm@474: in 2D pixel-space.
rlm@474: 
rlm@517:     #+caption: Program to efficiently determine point inclusion 
rlm@475:     #+caption: in a triangle.
rlm@475:     #+name: in-triangle
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn convex-bounds
rlm@474:   "Returns the smallest square containing the given vertices, as a
rlm@474:    vector of integers [left top width height]."
rlm@474:   [verts]
rlm@474:   (let [xs (map first verts)
rlm@474:         ys (map second verts)
rlm@474:         x0 (Math/floor (apply min xs))
rlm@474:         y0 (Math/floor (apply min ys))
rlm@474:         x1 (Math/ceil (apply max xs))
rlm@474:         y1 (Math/ceil (apply max ys))]
rlm@474:     [x0 y0 (- x1 x0) (- y1 y0)]))
rlm@474: 
rlm@474: (defn same-side?
rlm@474:   "Given the points p1 and p2 and the reference point ref, is point p
rlm@474:   on the same side of the line that goes through p1 and p2 as ref is?" 
rlm@474:   [p1 p2 ref p]
rlm@474:   (<=
rlm@474:    0
rlm@474:    (.dot 
rlm@474:     (.cross (.subtract p2 p1) (.subtract p p1))
rlm@474:     (.cross (.subtract p2 p1) (.subtract ref p1)))))
rlm@474: 
rlm@474: (defn inside-triangle?
rlm@474:   "Is the point inside the triangle?"
rlm@474:   {:author "Dylan Holmes"}
rlm@474:   [#^Triangle tri #^Vector3f p]
rlm@474:   (let [[vert-1 vert-2 vert-3] [(.get1 tri) (.get2 tri) (.get3 tri)]]
rlm@474:     (and
rlm@474:      (same-side? vert-1 vert-2 vert-3 p)
rlm@474:      (same-side? vert-2 vert-3 vert-1 p)
rlm@474:      (same-side? vert-3 vert-1 vert-2 p))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@477: *** Feeler Coordinates
rlm@474: 
rlm@475:     The triangle-related functions above make short work of
rlm@475:     calculating the positions and orientations of each feeler in
rlm@475:     world-space.
rlm@474: 
rlm@475:     #+caption: Program to get the coordinates of ``feelers '' in 
rlm@475:     #+caption: both world and UV-coordinates.
rlm@475:     #+name: feeler-coordinates
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn feeler-pixel-coords
rlm@474:  "Returns the coordinates of the feelers in pixel space in lists, one
rlm@474:   list for each triangle, ordered in the same way as (triangles) and
rlm@474:   (pixel-triangles)."
rlm@474:  [#^Geometry geo image]
rlm@474:  (map 
rlm@474:   (fn [pixel-triangle]
rlm@474:     (filter
rlm@474:      (fn [coord]
rlm@474:        (inside-triangle? (->triangle pixel-triangle)
rlm@474:                          (->vector3f coord)))
rlm@474:        (white-coordinates image (convex-bounds pixel-triangle))))
rlm@474:   (pixel-triangles geo image)))
rlm@474: 
rlm@474: (defn feeler-world-coords 
rlm@474:  "Returns the coordinates of the feelers in world space in lists, one
rlm@474:   list for each triangle, ordered in the same way as (triangles) and
rlm@474:   (pixel-triangles)."
rlm@474:  [#^Geometry geo image]
rlm@474:  (let [transforms
rlm@474:        (map #(triangles->affine-transform
rlm@474:               (->triangle %1) (->triangle %2))
rlm@474:             (pixel-triangles geo image)
rlm@474:             (triangles geo))]
rlm@474:    (map (fn [transform coords]
rlm@474:           (map #(.mult transform (->vector3f %)) coords))
rlm@474:         transforms (feeler-pixel-coords geo image))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@475:     #+caption: Program to get the position of the base and tip of 
rlm@475:     #+caption: each ``feeler''
rlm@475:     #+name: feeler-tips
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn feeler-origins
rlm@474:   "The world space coordinates of the root of each feeler."
rlm@474:   [#^Geometry geo image]
rlm@474:    (reduce concat (feeler-world-coords geo image)))
rlm@474: 
rlm@474: (defn feeler-tips
rlm@474:   "The world space coordinates of the tip of each feeler."
rlm@474:   [#^Geometry geo image]
rlm@474:   (let [world-coords (feeler-world-coords geo image)
rlm@474:         normals
rlm@474:         (map
rlm@474:          (fn [triangle]
rlm@474:            (.calculateNormal triangle)
rlm@474:            (.clone (.getNormal triangle)))
rlm@474:          (map ->triangle (triangles geo)))]
rlm@474: 
rlm@474:     (mapcat (fn [origins normal]
rlm@474:               (map #(.add % normal) origins))
rlm@474:             world-coords normals)))
rlm@474: 
rlm@474: (defn touch-topology
rlm@474:   [#^Geometry geo image]
rlm@474:   (collapse (reduce concat (feeler-pixel-coords geo image))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@477: *** Simulated Touch
rlm@474: 
rlm@475:     Now that the functions to construct feelers are complete,
rlm@475:     =touch-kernel= generates functions to be called from within a
rlm@475:     simulation that perform the necessary physics collisions to
rlm@475:     collect tactile data, and =touch!= recursively applies it to every
rlm@475:     node in the creature.
rlm@474: 
rlm@475:     #+caption: Efficient program to transform a ray from 
rlm@475:     #+caption: one position to another.
rlm@475:     #+name: set-ray
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn set-ray [#^Ray ray #^Matrix4f transform
rlm@474:                #^Vector3f origin #^Vector3f tip]
rlm@474:   ;; Doing everything locally reduces garbage collection by enough to
rlm@474:   ;; be worth it.
rlm@474:   (.mult transform origin (.getOrigin ray))
rlm@474:   (.mult transform tip (.getDirection ray))
rlm@474:   (.subtractLocal (.getDirection ray) (.getOrigin ray))
rlm@474:   (.normalizeLocal (.getDirection ray)))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@475:     #+caption: This is the core of touch in =CORTEX= each feeler 
rlm@475:     #+caption: follows the object it is bound to, reporting any 
rlm@475:     #+caption: collisions that may happen.
rlm@475:     #+name: touch-kernel
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn touch-kernel
rlm@474:   "Constructs a function which will return tactile sensory data from
rlm@474:    'geo when called from inside a running simulation"
rlm@474:   [#^Geometry geo]
rlm@474:   (if-let
rlm@474:       [profile (tactile-sensor-profile geo)]
rlm@474:     (let [ray-reference-origins (feeler-origins geo profile)
rlm@474:           ray-reference-tips (feeler-tips geo profile)
rlm@474:           ray-length (tactile-scale geo)
rlm@474:           current-rays (map (fn [_] (Ray.)) ray-reference-origins)
rlm@474:           topology (touch-topology geo profile)
rlm@474:           correction (float (* ray-length -0.2))]
rlm@474:       ;; slight tolerance for very close collisions.
rlm@474:       (dorun
rlm@474:        (map (fn [origin tip]
rlm@474:               (.addLocal origin (.mult (.subtract tip origin)
rlm@474:                                        correction)))
rlm@474:             ray-reference-origins ray-reference-tips))
rlm@474:       (dorun (map #(.setLimit % ray-length) current-rays))
rlm@474:       (fn [node]
rlm@474:         (let [transform (.getWorldMatrix geo)]
rlm@474:           (dorun
rlm@474:            (map (fn [ray ref-origin ref-tip]
rlm@474:                   (set-ray ray transform ref-origin ref-tip))
rlm@474:                 current-rays ray-reference-origins
rlm@474:                 ray-reference-tips))
rlm@474:           (vector
rlm@474:            topology
rlm@474:            (vec
rlm@474:             (for [ray current-rays]
rlm@474:               (do
rlm@474:                 (let [results (CollisionResults.)]
rlm@474:                   (.collideWith node ray results)
rlm@474:                   (let [touch-objects
rlm@474:                         (filter #(not (= geo (.getGeometry %)))
rlm@474:                                 results)
rlm@474:                         limit (.getLimit ray)]
rlm@474:                     [(if (empty? touch-objects)
rlm@474:                        limit
rlm@474:                        (let [response
rlm@474:                              (apply min (map #(.getDistance %)
rlm@474:                                              touch-objects))]
rlm@474:                          (FastMath/clamp
rlm@474:                           (float 
rlm@474:                            (if (> response limit) (float 0.0)
rlm@474:                                (+ response correction)))
rlm@474:                            (float 0.0)
rlm@474:                            limit)))
rlm@474:                      limit])))))))))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@474: 
rlm@475:     Armed with the =touch!= function, =CORTEX= becomes capable of
rlm@475:     giving creatures a sense of touch. A simple test is to create a
rlm@517:     cube that is outfitted with a uniform distribution of touch
rlm@475:     sensors. It can feel the ground and any balls that it touches.
rlm@475: 
rlm@475:     #+caption: =CORTEX= interface for creating touch in a simulated
rlm@475:     #+caption: creature.
rlm@475:     #+name: touch
rlm@475:     #+begin_listing clojure
rlm@475:     #+BEGIN_SRC clojure
rlm@474: (defn touch! 
rlm@474:   "Endow the creature with the sense of touch. Returns a sequence of
rlm@474:    functions, one for each body part with a tactile-sensor-profile,
rlm@474:    each of which when called returns sensory data for that body part."
rlm@474:   [#^Node creature]
rlm@474:   (filter
rlm@474:    (comp not nil?)
rlm@474:    (map touch-kernel
rlm@474:         (filter #(isa? (class %) Geometry)
rlm@474:                 (node-seq creature)))))
rlm@475:     #+END_SRC
rlm@475:     #+end_listing
rlm@475:     
rlm@475:     The tactile-sensor-profile image for the touch cube is a simple
rlm@517:     cross with a uniform distribution of touch sensors:
rlm@474: 
rlm@475:     #+caption: The touch profile for the touch-cube. Each pure white 
rlm@475:     #+caption: pixel defines a touch sensitive feeler.
rlm@475:     #+name: touch-cube-uv-map
rlm@495:     #+ATTR_LaTeX: :width 7cm
rlm@475:     [[./images/touch-profile.png]]
rlm@474: 
rlm@517:     #+caption: The touch cube reacts to cannonballs. The black, red, 
rlm@475:     #+caption: and white cross on the right is a visual display of 
rlm@475:     #+caption: the creature's touch. White means that it is feeling 
rlm@475:     #+caption: something strongly, black is not feeling anything,
rlm@475:     #+caption: and gray is in-between. The cube can feel both the 
rlm@475:     #+caption: floor and the ball. Notice that when the ball causes 
rlm@475:     #+caption: the cube to tip, that the bottom face can still feel 
rlm@475:     #+caption: part of the ground.
rlm@518:     #+name: touch-cube-uv-map-2
rlm@475:     #+ATTR_LaTeX: :width 15cm
rlm@475:     [[./images/touch-cube.png]]
rlm@474: 
rlm@511: ** Proprioception provides knowledge of your own body's position
rlm@436: 
rlm@479:    Close your eyes, and touch your nose with your right index finger.
rlm@479:    How did you do it? You could not see your hand, and neither your
rlm@479:    hand nor your nose could use the sense of touch to guide the path
rlm@479:    of your hand. There are no sound cues, and Taste and Smell
rlm@479:    certainly don't provide any help. You know where your hand is
rlm@479:    without your other senses because of Proprioception.
rlm@479:    
rlm@479:    Humans can sometimes loose this sense through viral infections or
rlm@479:    damage to the spinal cord or brain, and when they do, they loose
rlm@479:    the ability to control their own bodies without looking directly at
rlm@479:    the parts they want to move. In [[http://en.wikipedia.org/wiki/The_Man_Who_Mistook_His_Wife_for_a_Hat][The Man Who Mistook His Wife for a
rlm@518:    Hat]] (\cite{man-wife-hat}), a woman named Christina looses this
rlm@518:    sense and has to learn how to move by carefully watching her arms
rlm@518:    and legs. She describes proprioception as the "eyes of the body,
rlm@518:    the way the body sees itself".
rlm@479:    
rlm@479:    Proprioception in humans is mediated by [[http://en.wikipedia.org/wiki/Articular_capsule][joint capsules]], [[http://en.wikipedia.org/wiki/Muscle_spindle][muscle
rlm@479:    spindles]], and the [[http://en.wikipedia.org/wiki/Golgi_tendon_organ][Golgi tendon organs]]. These measure the relative
rlm@479:    positions of each body part by monitoring muscle strain and length.
rlm@479:    
rlm@479:    It's clear that this is a vital sense for fluid, graceful movement.
rlm@479:    It's also particularly easy to implement in jMonkeyEngine.
rlm@479:    
rlm@479:    My simulated proprioception calculates the relative angles of each
rlm@562:    joint from the rest position defined in the Blender file. This
rlm@479:    simulates the muscle-spindles and joint capsules. I will deal with
rlm@479:    Golgi tendon organs, which calculate muscle strain, in the next
rlm@566:    section (2.12).
rlm@479: 
rlm@479: *** Helper functions
rlm@479: 
rlm@479:     =absolute-angle= calculates the angle between two vectors,
rlm@479:     relative to a third axis vector. This angle is the number of
rlm@479:     radians you have to move counterclockwise around the axis vector
rlm@479:     to get from the first to the second vector. It is not commutative
rlm@479:     like a normal dot-product angle is.
rlm@479: 
rlm@479:     The purpose of these functions is to build a system of angle
rlm@517:     measurement that is biologically plausible.
rlm@479: 
rlm@479:     #+caption: Program to measure angles along a vector
rlm@479:     #+name: helpers
rlm@479:     #+begin_listing clojure
rlm@479:     #+BEGIN_SRC clojure
rlm@479: (defn right-handed?
rlm@479:   "true iff the three vectors form a right handed coordinate
rlm@479:    system. The three vectors do not have to be normalized or
rlm@479:    orthogonal."
rlm@479:   [vec1 vec2 vec3]
rlm@479:   (pos? (.dot (.cross vec1 vec2) vec3)))
rlm@479: 
rlm@479: (defn absolute-angle
rlm@479:   "The angle between 'vec1 and 'vec2 around 'axis. In the range 
rlm@479:    [0 (* 2 Math/PI)]."
rlm@479:   [vec1 vec2 axis]
rlm@479:   (let [angle (.angleBetween vec1 vec2)]
rlm@479:     (if (right-handed? vec1 vec2 axis)
rlm@479:       angle (- (* 2 Math/PI) angle))))
rlm@479:     #+END_SRC
rlm@479:     #+end_listing
rlm@479: 
rlm@479: *** Proprioception Kernel
rlm@479:     
rlm@479:     Given a joint, =proprioception-kernel= produces a function that
rlm@545:     calculates the Euler angles between the objects the joint
rlm@479:     connects. The only tricky part here is making the angles relative
rlm@479:     to the joint's initial ``straightness''.
rlm@479: 
rlm@517:     #+caption: Program to return biologically reasonable proprioceptive
rlm@479:     #+caption: data for each joint.
rlm@479:     #+name: proprioception
rlm@479:     #+begin_listing clojure
rlm@479:     #+BEGIN_SRC clojure
rlm@479: (defn proprioception-kernel
rlm@479:   "Returns a function which returns proprioceptive sensory data when
rlm@479:   called inside a running simulation."
rlm@479:   [#^Node parts #^Node joint]
rlm@479:   (let [[obj-a obj-b] (joint-targets parts joint)
rlm@479:         joint-rot (.getWorldRotation joint)
rlm@479:         x0 (.mult joint-rot Vector3f/UNIT_X)
rlm@479:         y0 (.mult joint-rot Vector3f/UNIT_Y)
rlm@479:         z0 (.mult joint-rot Vector3f/UNIT_Z)]
rlm@479:     (fn []
rlm@479:       (let [rot-a (.clone (.getWorldRotation obj-a))
rlm@479:             rot-b (.clone (.getWorldRotation obj-b))
rlm@479:             x (.mult rot-a x0)
rlm@479:             y (.mult rot-a y0)
rlm@479:             z (.mult rot-a z0)
rlm@479: 
rlm@479:             X (.mult rot-b x0)
rlm@479:             Y (.mult rot-b y0)
rlm@479:             Z (.mult rot-b z0)
rlm@479:             heading  (Math/atan2 (.dot X z) (.dot X x))
rlm@479:             pitch  (Math/atan2 (.dot X y) (.dot X x))
rlm@479: 
rlm@479:             ;; rotate x-vector back to origin
rlm@479:             reverse
rlm@479:             (doto (Quaternion.)
rlm@479:               (.fromAngleAxis
rlm@479:                (.angleBetween X x)
rlm@479:                (let [cross (.normalize (.cross X x))]
rlm@479:                  (if (= 0 (.length cross)) y cross))))
rlm@479:             roll (absolute-angle (.mult reverse Y) y x)]
rlm@479:         [heading pitch roll]))))
rlm@479: 
rlm@479: (defn proprioception!
rlm@479:   "Endow the creature with the sense of proprioception. Returns a
rlm@479:    sequence of functions, one for each child of the \"joints\" node in
rlm@479:    the creature, which each report proprioceptive information about
rlm@479:    that joint."
rlm@479:   [#^Node creature]
rlm@479:   ;; extract the body's joints
rlm@479:   (let [senses (map (partial proprioception-kernel creature)
rlm@479:                     (joints creature))]
rlm@479:     (fn []
rlm@479:       (map #(%) senses))))
rlm@479:     #+END_SRC
rlm@479:     #+end_listing
rlm@479: 
rlm@479:     =proprioception!= maps =proprioception-kernel= across all the
rlm@479:     joints of the creature. It uses the same list of joints that
rlm@479:     =joints= uses. Proprioception is the easiest sense to implement in
rlm@479:     =CORTEX=, and it will play a crucial role when efficiently
rlm@479:     implementing empathy.
rlm@479: 
rlm@479:     #+caption: In the upper right corner, the three proprioceptive
rlm@479:     #+caption: angle measurements are displayed. Red is yaw, Green is 
rlm@479:     #+caption: pitch, and White is roll.
rlm@479:     #+name: proprio
rlm@479:     #+ATTR_LaTeX: :width 11cm
rlm@479:     [[./images/proprio.png]]
rlm@479: 
rlm@511: ** Muscles contain both sensors and effectors
rlm@481: 
rlm@481:    Surprisingly enough, terrestrial creatures only move by using
rlm@481:    torque applied about their joints. There's not a single straight
rlm@481:    line of force in the human body at all! (A straight line of force
rlm@481:    would correspond to some sort of jet or rocket propulsion.)
rlm@481:    
rlm@481:    In humans, muscles are composed of muscle fibers which can contract
rlm@481:    to exert force. The muscle fibers which compose a muscle are
rlm@481:    partitioned into discrete groups which are each controlled by a
rlm@481:    single alpha motor neuron. A single alpha motor neuron might
rlm@481:    control as little as three or as many as one thousand muscle
rlm@481:    fibers. When the alpha motor neuron is engaged by the spinal cord,
rlm@481:    it activates all of the muscle fibers to which it is attached. The
rlm@481:    spinal cord generally engages the alpha motor neurons which control
rlm@481:    few muscle fibers before the motor neurons which control many
rlm@481:    muscle fibers. This recruitment strategy allows for precise
rlm@481:    movements at low strength. The collection of all motor neurons that
rlm@481:    control a muscle is called the motor pool. The brain essentially
rlm@481:    says "activate 30% of the motor pool" and the spinal cord recruits
rlm@481:    motor neurons until 30% are activated. Since the distribution of
rlm@481:    power among motor neurons is unequal and recruitment goes from
rlm@481:    weakest to strongest, the first 30% of the motor pool might be 5%
rlm@481:    of the strength of the muscle.
rlm@481:    
rlm@481:    My simulated muscles follow a similar design: Each muscle is
rlm@481:    defined by a 1-D array of numbers (the "motor pool"). Each entry in
rlm@481:    the array represents a motor neuron which controls a number of
rlm@481:    muscle fibers equal to the value of the entry. Each muscle has a
rlm@481:    scalar strength factor which determines the total force the muscle
rlm@481:    can exert when all motor neurons are activated. The effector
rlm@481:    function for a muscle takes a number to index into the motor pool,
rlm@481:    and then "activates" all the motor neurons whose index is lower or
rlm@481:    equal to the number. Each motor-neuron will apply force in
rlm@481:    proportion to its value in the array. Lower values cause less
rlm@481:    force. The lower values can be put at the "beginning" of the 1-D
rlm@481:    array to simulate the layout of actual human muscles, which are
rlm@481:    capable of more precise movements when exerting less force. Or, the
rlm@481:    motor pool can simulate more exotic recruitment strategies which do
rlm@481:    not correspond to human muscles.
rlm@481:    
rlm@481:    This 1D array is defined in an image file for ease of
rlm@481:    creation/visualization. Here is an example muscle profile image.
rlm@481: 
rlm@481:    #+caption: A muscle profile image that describes the strengths
rlm@481:    #+caption: of each motor neuron in a muscle. White is weakest 
rlm@481:    #+caption: and dark red is strongest. This particular pattern 
rlm@481:    #+caption: has weaker motor neurons at the beginning, just 
rlm@481:    #+caption: like human muscle.
rlm@481:    #+name: muscle-recruit
rlm@481:    #+ATTR_LaTeX: :width 7cm
rlm@481:    [[./images/basic-muscle.png]]
rlm@481: 
rlm@481: *** Muscle meta-data
rlm@481: 
rlm@562:     #+caption: Program to deal with loading muscle data from a Blender
rlm@481:     #+caption: file's metadata.
rlm@481:     #+name: motor-pool
rlm@481:     #+begin_listing clojure
rlm@481:     #+BEGIN_SRC clojure
rlm@481: (defn muscle-profile-image
rlm@567:   "Get the muscle-profile image from the node's Blender meta-data."
rlm@481:   [#^Node muscle]
rlm@481:   (if-let [image (meta-data muscle "muscle")]
rlm@481:     (load-image image)))
rlm@481: 
rlm@481: (defn muscle-strength
rlm@481:   "Return the strength of this muscle, or 1 if it is not defined."
rlm@481:   [#^Node muscle]
rlm@481:   (if-let [strength (meta-data muscle "strength")]
rlm@481:     strength 1))
rlm@481: 
rlm@481: (defn motor-pool
rlm@481:   "Return a vector where each entry is the strength of the \"motor
rlm@481:    neuron\" at that part in the muscle."
rlm@481:   [#^Node muscle]
rlm@481:   (let [profile (muscle-profile-image muscle)]
rlm@481:     (vec
rlm@481:      (let [width (.getWidth profile)]
rlm@481:        (for [x (range width)]
rlm@481:        (- 255
rlm@481:           (bit-and
rlm@481:            0x0000FF
rlm@481:            (.getRGB profile x 0))))))))
rlm@481:     #+END_SRC
rlm@481:     #+end_listing
rlm@481: 
rlm@481:     Of note here is =motor-pool= which interprets the muscle-profile
rlm@481:     image in a way that allows me to use gradients between white and
rlm@481:     red, instead of shades of gray as I've been using for all the
rlm@481:     other senses. This is purely an aesthetic touch.
rlm@481: 
rlm@481: *** Creating muscles
rlm@481: 
rlm@517:     #+caption: This is the core movement function in =CORTEX=, which
rlm@481:     #+caption: implements muscles that report on their activation.
rlm@481:     #+name: muscle-kernel
rlm@481:     #+begin_listing clojure
rlm@481:     #+BEGIN_SRC clojure
rlm@481: (defn movement-kernel
rlm@481:   "Returns a function which when called with a integer value inside a
rlm@481:    running simulation will cause movement in the creature according
rlm@481:    to the muscle's position and strength profile. Each function
rlm@481:    returns the amount of force applied / max force."
rlm@481:   [#^Node creature #^Node muscle]
rlm@481:   (let [target (closest-node creature muscle)
rlm@481:         axis
rlm@481:         (.mult (.getWorldRotation muscle) Vector3f/UNIT_Y)
rlm@481:         strength (muscle-strength muscle)
rlm@481:         
rlm@481:         pool (motor-pool muscle)
rlm@481:         pool-integral (reductions + pool)
rlm@481:         forces
rlm@481:         (vec (map  #(float (* strength (/ % (last pool-integral))))
rlm@481:               pool-integral))
rlm@481:         control (.getControl target RigidBodyControl)]
rlm@481:     (fn [n]
rlm@481:       (let [pool-index (max 0 (min n (dec (count pool))))
rlm@481:             force (forces pool-index)]
rlm@481:         (.applyTorque control (.mult axis force))
rlm@481:         (float (/ force strength))))))
rlm@481: 
rlm@481: (defn movement!
rlm@481:   "Endow the creature with the power of movement. Returns a sequence
rlm@481:    of functions, each of which accept an integer value and will
rlm@481:    activate their corresponding muscle."
rlm@481:   [#^Node creature]
rlm@481:     (for [muscle (muscles creature)]
rlm@481:       (movement-kernel creature muscle)))
rlm@481:     #+END_SRC
rlm@481:     #+end_listing
rlm@481: 
rlm@481: 
rlm@531:     =movement-kernel= creates a function that controls the movement
rlm@525:     of the nearest physical node to the muscle node. The muscle exerts
rlm@525:     a rotational force dependent on it's orientation to the object in
rlm@562:     the Blender file. The function returned by =movement-kernel= is
rlm@525:     also a sense function: it returns the percent of the total muscle
rlm@525:     strength that is currently being employed. This is analogous to
rlm@525:     muscle tension in humans and completes the sense of proprioception
rlm@551:     begun in the last chapter.
rlm@488:     
rlm@507: ** =CORTEX= brings complex creatures to life!
rlm@483:    
rlm@483:    The ultimate test of =CORTEX= is to create a creature with the full
rlm@483:    gamut of senses and put it though its paces. 
rlm@483: 
rlm@483:    With all senses enabled, my right hand model looks like an
rlm@483:    intricate marionette hand with several strings for each finger:
rlm@483: 
rlm@483:    #+caption: View of the hand model with all sense nodes. You can see 
rlm@517:    #+caption: the joint, muscle, ear, and eye nodes here.
rlm@483:    #+name: hand-nodes-1
rlm@483:    #+ATTR_LaTeX: :width 11cm
rlm@483:    [[./images/hand-with-all-senses2.png]]
rlm@483: 
rlm@483:    #+caption: An alternate view of the hand.
rlm@483:    #+name: hand-nodes-2
rlm@484:    #+ATTR_LaTeX: :width 15cm
rlm@484:    [[./images/hand-with-all-senses3.png]]
rlm@484: 
rlm@484:    With the hand fully rigged with senses, I can run it though a test
rlm@484:    that will test everything. 
rlm@484: 
rlm@553:    #+caption: Selected frames from a full test of the hand with all
rlm@553:    #+caption: senses. Note especially the interactions the hand has
rlm@553:    #+caption: with itself: it feels its own palm and fingers, and when
rlm@553:    #+caption: it curls its fingers, it sees them with its eye (which
rlm@553:    #+caption: is located in the center of the palm. The red block
rlm@553:    #+caption: appears with a pure tone sound. The hand then uses its
rlm@553:    #+caption: muscles to launch the cube!
rlm@484:    #+name: integration
rlm@553:    #+ATTR_LaTeX: :width 15cm
rlm@484:    [[./images/integration.png]]
rlm@436: 
rlm@517: ** =CORTEX= enables many possibilities for further research
rlm@485: 
rlm@485:    Often times, the hardest part of building a system involving
rlm@485:    creatures is dealing with physics and graphics. =CORTEX= removes
rlm@485:    much of this initial difficulty and leaves researchers free to
rlm@564:    directly pursue their ideas. I hope that even novices with a
rlm@485:    passing curiosity about simulated touch or creature evolution will
rlm@485:    be able to use cortex for experimentation. =CORTEX= is a completely
rlm@485:    simulated world, and far from being a disadvantage, its simulated
rlm@485:    nature enables you to create senses and creatures that would be
rlm@485:    impossible to make in the real world.
rlm@485: 
rlm@485:    While not by any means a complete list, here are some paths
rlm@485:    =CORTEX= is well suited to help you explore:
rlm@485: 
rlm@485:    - Empathy         :: my empathy program leaves many areas for
rlm@485:         improvement, among which are using vision to infer
rlm@485:         proprioception and looking up sensory experience with imagined
rlm@485:         vision, touch, and sound.
rlm@547:    - Evolution :: Karl Sims created a rich environment for simulating
rlm@547:         the evolution of creatures on a Connection Machine
rlm@547:         (\cite{sims-evolving-creatures}). Today, this can be redone
rlm@547:         and expanded with =CORTEX= on an ordinary computer.
rlm@485:    - Exotic senses  :: Cortex enables many fascinating senses that are
rlm@485:         not possible to build in the real world. For example,
rlm@485:         telekinesis is an interesting avenue to explore. You can also
rlm@485:         make a ``semantic'' sense which looks up metadata tags on
rlm@485:         objects in the environment the metadata tags might contain
rlm@485:         other sensory information.
rlm@485:    - Imagination via subworlds :: this would involve a creature with
rlm@485:         an effector which creates an entire new sub-simulation where
rlm@485:         the creature has direct control over placement/creation of
rlm@485:         objects via simulated telekinesis. The creature observes this
rlm@547:         sub-world through its normal senses and uses its observations
rlm@485:         to make predictions about its top level world.
rlm@485:    - Simulated prescience :: step the simulation forward a few ticks,
rlm@485:         gather sensory data, then supply this data for the creature as
rlm@485:         one of its actual senses. The cost of prescience is slowing
rlm@485:         the simulation down by a factor proportional to however far
rlm@485:         you want the entities to see into the future. What happens
rlm@485:         when two evolved creatures that can each see into the future
rlm@485:         fight each other?
rlm@485:    - Swarm creatures :: Program a group of creatures that cooperate
rlm@485:         with each other. Because the creatures would be simulated, you
rlm@485:         could investigate computationally complex rules of behavior
rlm@485:         which still, from the group's point of view, would happen in
rlm@547:         real time. Interactions could be as simple as cellular
rlm@485:         organisms communicating via flashing lights, or as complex as
rlm@485:         humanoids completing social tasks, etc.
rlm@547:    - =HACKER= for writing muscle-control programs :: Presented with a
rlm@547:         low-level muscle control / sense API, generate higher level
rlm@485:         programs for accomplishing various stated goals. Example goals
rlm@485:         might be "extend all your fingers" or "move your hand into the
rlm@485:         area with blue light" or "decrease the angle of this joint".
rlm@485:         It would be like Sussman's HACKER, except it would operate
rlm@485:         with much more data in a more realistic world. Start off with
rlm@485:         "calisthenics" to develop subroutines over the motor control
rlm@547:         API. The low level programming code might be a turning machine
rlm@547:         that could develop programs to iterate over a "tape" where
rlm@547:         each entry in the tape could control recruitment of the fibers
rlm@547:         in a muscle.
rlm@547:    - Sense fusion :: There is much work to be done on sense
rlm@485:         integration -- building up a coherent picture of the world and
rlm@547:         the things in it. With =CORTEX= as a base, you can explore
rlm@485:         concepts like self-organizing maps or cross modal clustering
rlm@485:         in ways that have never before been tried.
rlm@485:    - Inverse kinematics :: experiments in sense guided motor control
rlm@485:         are easy given =CORTEX='s support -- you can get right to the
rlm@485:         hard control problems without worrying about physics or
rlm@485:         senses.
rlm@485: 
rlm@525: \newpage
rlm@525: 
rlm@558: * =EMPATH=: action recognition in a simulated worm
rlm@435: 
rlm@449:   Here I develop a computational model of empathy, using =CORTEX= as a
rlm@449:   base. Empathy in this context is the ability to observe another
rlm@449:   creature and infer what sorts of sensations that creature is
rlm@449:   feeling. My empathy algorithm involves multiple phases. First is
rlm@449:   free-play, where the creature moves around and gains sensory
rlm@449:   experience. From this experience I construct a representation of the
rlm@449:   creature's sensory state space, which I call \Phi-space. Using
rlm@449:   \Phi-space, I construct an efficient function which takes the
rlm@449:   limited data that comes from observing another creature and enriches
rlm@525:   it with a full compliment of imagined sensory data. I can then use
rlm@525:   the imagined sensory data to recognize what the observed creature is
rlm@449:   doing and feeling, using straightforward embodied action predicates.
rlm@449:   This is all demonstrated with using a simple worm-like creature, and
rlm@449:   recognizing worm-actions based on limited data.
rlm@449: 
rlm@555:   #+caption: Here is the worm with which we will be working. It is
rlm@555:   #+caption: composed of 5 segments. Each segment has a pair of
rlm@555:   #+caption: extensor and flexor muscles. Each of the worm's four
rlm@555:   #+caption: joints is a hinge joint which allows about 30 degrees of
rlm@555:   #+caption: rotation to either side. Each segment of the worm is
rlm@555:   #+caption: touch-capable and has a uniform distribution of touch
rlm@555:   #+caption: sensors on each of its faces. Each joint has a
rlm@555:   #+caption: proprioceptive sense to detect relative positions. The
rlm@555:   #+caption: worm segments are all the same except for the first one,
rlm@555:   #+caption: which has a much higher weight than the others to allow
rlm@555:   #+caption: for easy manual motor control.
rlm@449:   #+name: basic-worm-view
rlm@449:   #+ATTR_LaTeX: :width 10cm
rlm@449:   [[./images/basic-worm-view.png]]
rlm@449: 
rlm@562:   #+caption: Program for reading a worm from a Blender file and 
rlm@449:   #+caption: outfitting it with the senses of proprioception, 
rlm@449:   #+caption: touch, and the ability to move, as specified in the 
rlm@562:   #+caption: Blender file.
rlm@449:   #+name: get-worm
rlm@449:   #+begin_listing clojure
rlm@449:   #+begin_src clojure
rlm@449: (defn worm []
rlm@449:   (let [model (load-blender-model "Models/worm/worm.blend")]
rlm@449:     {:body (doto model (body!))
rlm@449:      :touch (touch! model)
rlm@449:      :proprioception (proprioception! model)
rlm@449:      :muscles (movement! model)}))
rlm@449:   #+end_src
rlm@449:   #+end_listing
rlm@452: 
rlm@531: ** Embodiment factors action recognition into manageable parts
rlm@435: 
rlm@449:    Using empathy, I divide the problem of action recognition into a
rlm@449:    recognition process expressed in the language of a full compliment
rlm@517:    of senses, and an imaginative process that generates full sensory
rlm@449:    data from partial sensory data. Splitting the action recognition
rlm@449:    problem in this manner greatly reduces the total amount of work to
rlm@517:    recognize actions: The imaginative process is mostly just matching
rlm@449:    previous experience, and the recognition process gets to use all
rlm@449:    the senses to directly describe any action.
rlm@449: 
rlm@436: ** Action recognition is easy with a full gamut of senses
rlm@435: 
rlm@554:    Embodied representation using multiple senses such as touch,
rlm@545:    proprioception, and muscle tension turns out be exceedingly
rlm@525:    efficient at describing body-centered actions. It is the right
rlm@525:    language for the job. For example, it takes only around 5 lines of
rlm@554:    clojure code to describe the action of curling using embodied
rlm@451:    primitives. It takes about 10 lines to describe the seemingly
rlm@449:    complicated action of wiggling.
rlm@449: 
rlm@449:    The following action predicates each take a stream of sensory
rlm@449:    experience, observe however much of it they desire, and decide
rlm@449:    whether the worm is doing the action they describe. =curled?=
rlm@449:    relies on proprioception, =resting?= relies on touch, =wiggling?=
rlm@517:    relies on a Fourier analysis of muscle contraction, and
rlm@525:    =grand-circle?= relies on touch and reuses =curled?= in its
rlm@525:    definition, showing how embodied predicates can be composed.
rlm@449:    
rlm@530:    
rlm@449:    #+caption: Program for detecting whether the worm is curled. This is the 
rlm@449:    #+caption: simplest action predicate, because it only uses the last frame 
rlm@449:    #+caption: of sensory experience, and only uses proprioceptive data. Even 
rlm@449:    #+caption: this simple predicate, however, is automatically frame 
rlm@530:    #+caption: independent and ignores vermopomorphic\protect\footnotemark  
rlm@532:    #+caption: \space differences such as worm textures and colors.
rlm@449:    #+name: curled
rlm@509:    #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (defn curled?
rlm@449:   "Is the worm curled up?"
rlm@449:   [experiences]
rlm@449:   (every?
rlm@449:    (fn [[_ _ bend]]
rlm@449:      (> (Math/sin bend) 0.64))
rlm@449:    (:proprioception (peek experiences))))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@530:    
rlm@530:    #+BEGIN_LaTeX
rlm@530:    \footnotetext{Like \emph{anthropomorphic} except for worms instead of humans.}
rlm@530:    #+END_LaTeX
rlm@449: 
rlm@449:    #+caption: Program for summarizing the touch information in a patch 
rlm@449:    #+caption: of skin.
rlm@449:    #+name: touch-summary
rlm@509:    #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (defn contact
rlm@449:   "Determine how much contact a particular worm segment has with
rlm@449:    other objects. Returns a value between 0 and 1, where 1 is full
rlm@449:    contact and 0 is no contact."
rlm@449:   [touch-region [coords contact :as touch]]
rlm@449:   (-> (zipmap coords contact)
rlm@449:       (select-keys touch-region)
rlm@449:       (vals)
rlm@449:       (#(map first %))
rlm@449:       (average)
rlm@449:       (* 10)
rlm@449:       (- 1)
rlm@449:       (Math/abs)))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@449: 
rlm@449: 
rlm@449:    #+caption: Program for detecting whether the worm is at rest. This program
rlm@449:    #+caption: uses a summary of the tactile information from the underbelly 
rlm@449:    #+caption: of the worm, and is only true if every segment is touching the 
rlm@449:    #+caption: floor. Note that this function contains no references to 
rlm@517:    #+caption: proprioception at all.
rlm@449:    #+name: resting
rlm@452: #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (def worm-segment-bottom (rect-region [8 15] [14 22]))
rlm@449: 
rlm@449: (defn resting?
rlm@449:   "Is the worm resting on the ground?"
rlm@449:   [experiences]
rlm@449:   (every?
rlm@449:    (fn [touch-data]
rlm@449:      (< 0.9 (contact worm-segment-bottom touch-data)))
rlm@449:    (:touch (peek experiences))))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@449: 
rlm@449:    #+caption: Program for detecting whether the worm is curled up into a 
rlm@449:    #+caption: full circle. Here the embodied approach begins to shine, as
rlm@449:    #+caption: I am able to both use a previous action predicate (=curled?=)
rlm@449:    #+caption: as well as the direct tactile experience of the head and tail.
rlm@449:    #+name: grand-circle
rlm@452: #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))
rlm@449: 
rlm@449: (def worm-segment-top-tip (rect-region [0 15] [7 22]))
rlm@449: 
rlm@449: (defn grand-circle?
rlm@449:   "Does the worm form a majestic circle (one end touching the other)?"
rlm@449:   [experiences]
rlm@449:   (and (curled? experiences)
rlm@449:        (let [worm-touch (:touch (peek experiences))
rlm@449:              tail-touch (worm-touch 0)
rlm@449:              head-touch (worm-touch 4)]
rlm@449:          (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
rlm@449:               (< 0.55 (contact worm-segment-top-tip    head-touch))))))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@449: 
rlm@449: 
rlm@449:    #+caption: Program for detecting whether the worm has been wiggling for 
rlm@517:    #+caption: the last few frames. It uses a Fourier analysis of the muscle 
rlm@449:    #+caption: contractions of the worm's tail to determine wiggling. This is 
rlm@517:    #+caption: significant because there is no particular frame that clearly 
rlm@449:    #+caption: indicates that the worm is wiggling --- only when multiple frames 
rlm@449:    #+caption: are analyzed together is the wiggling revealed. Defining 
rlm@449:    #+caption: wiggling this way also gives the worm an opportunity to learn 
rlm@449:    #+caption: and recognize ``frustrated wiggling'', where the worm tries to 
rlm@449:    #+caption: wiggle but can't. Frustrated wiggling is very visually different 
rlm@449:    #+caption: from actual wiggling, but this definition gives it to us for free.
rlm@449:    #+name: wiggling
rlm@452: #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (defn fft [nums]
rlm@449:   (map
rlm@449:    #(.getReal %)
rlm@449:    (.transform
rlm@449:     (FastFourierTransformer. DftNormalization/STANDARD)
rlm@449:     (double-array nums) TransformType/FORWARD)))
rlm@449: 
rlm@449: (def indexed (partial map-indexed vector))
rlm@449: 
rlm@449: (defn max-indexed [s]
rlm@449:   (first (sort-by (comp - second) (indexed s))))
rlm@449: 
rlm@449: (defn wiggling?
rlm@449:   "Is the worm wiggling?"
rlm@449:   [experiences]
rlm@449:   (let [analysis-interval 0x40]
rlm@449:     (when (> (count experiences) analysis-interval)
rlm@449:       (let [a-flex 3
rlm@449:             a-ex   2
rlm@449:             muscle-activity
rlm@449:             (map :muscle (vector:last-n experiences analysis-interval))
rlm@449:             base-activity
rlm@449:             (map #(- (% a-flex) (% a-ex)) muscle-activity)]
rlm@449:         (= 2
rlm@449:            (first
rlm@449:             (max-indexed
rlm@449:              (map #(Math/abs %)
rlm@449:                   (take 20 (fft base-activity))))))))))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@449: 
rlm@449:    With these action predicates, I can now recognize the actions of
rlm@449:    the worm while it is moving under my control and I have access to
rlm@449:    all the worm's senses.
rlm@449: 
rlm@449:    #+caption: Use the action predicates defined earlier to report on 
rlm@449:    #+caption: what the worm is doing while in simulation.
rlm@449:    #+name: report-worm-activity
rlm@452: #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (defn debug-experience
rlm@449:   [experiences text]
rlm@449:   (cond
rlm@449:    (grand-circle? experiences) (.setText text "Grand Circle")
rlm@449:    (curled? experiences)       (.setText text "Curled")
rlm@449:    (wiggling? experiences)     (.setText text "Wiggling")
rlm@449:    (resting? experiences)      (.setText text "Resting")))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@449: 
rlm@449:    #+caption: Using =debug-experience=, the body-centered predicates
rlm@517:    #+caption: work together to classify the behavior of the worm. 
rlm@451:    #+caption: the predicates are operating with access to the worm's
rlm@451:    #+caption: full sensory data.
rlm@449:    #+name: basic-worm-view
rlm@449:    #+ATTR_LaTeX: :width 10cm
rlm@449:    [[./images/worm-identify-init.png]]
rlm@449: 
rlm@449:    These action predicates satisfy the recognition requirement of an
rlm@451:    empathic recognition system. There is power in the simplicity of
rlm@451:    the action predicates. They describe their actions without getting
rlm@531:    confused in visual details of the worm. Each one is independent of
rlm@531:    position and rotation, but more than that, they are each
rlm@531:    independent of irrelevant visual details of the worm and the
rlm@531:    environment. They will work regardless of whether the worm is a
rlm@531:    different color or heavily textured, or if the environment has
rlm@531:    strange lighting.
rlm@531: 
rlm@531:    Consider how the human act of jumping might be described with
rlm@531:    body-centered action predicates: You might specify that jumping is
rlm@531:    mainly the feeling of your knees bending, your thigh muscles
rlm@531:    contracting, and your inner ear experiencing a certain sort of back
rlm@531:    and forth acceleration. This representation is a very concrete
rlm@531:    description of jumping, couched in terms of muscles and senses, but
rlm@531:    it also has the ability to describe almost all kinds of jumping, a
rlm@531:    generality that you might think could only be achieved by a very
rlm@531:    abstract description. The body centered jumping predicate does not
rlm@531:    have terms that consider the color of a person's skin or whether
rlm@531:    they are male or female, instead it gets right to the meat of what
rlm@531:    jumping actually /is/.
rlm@531: 
rlm@531:    Of course, the action predicates are not directly applicable to
rlm@547:    video data, which lacks the advanced sensory information which they
rlm@531:    require!
rlm@449: 
rlm@449:    The trick now is to make the action predicates work even when the
rlm@554:    sensory data on which they depend is absent!
rlm@435: 
rlm@531: ** \Phi-space describes the worm's experiences
rlm@449:    
rlm@449:    As a first step towards building empathy, I need to gather all of
rlm@449:    the worm's experiences during free play. I use a simple vector to
rlm@449:    store all the experiences. 
rlm@449: 
rlm@449:    Each element of the experience vector exists in the vast space of
rlm@449:    all possible worm-experiences. Most of this vast space is actually
rlm@449:    unreachable due to physical constraints of the worm's body. For
rlm@449:    example, the worm's segments are connected by hinge joints that put
rlm@451:    a practical limit on the worm's range of motions without limiting
rlm@451:    its degrees of freedom. Some groupings of senses are impossible;
rlm@451:    the worm can not be bent into a circle so that its ends are
rlm@451:    touching and at the same time not also experience the sensation of
rlm@451:    touching itself.
rlm@449: 
rlm@451:    As the worm moves around during free play and its experience vector
rlm@451:    grows larger, the vector begins to define a subspace which is all
rlm@517:    the sensations the worm can practically experience during normal
rlm@451:    operation. I call this subspace \Phi-space, short for
rlm@451:    physical-space. The experience vector defines a path through
rlm@451:    \Phi-space. This path has interesting properties that all derive
rlm@533:    from physical embodiment. The proprioceptive components of the path
rlm@533:    vary smoothly, because in order for the worm to move from one
rlm@451:    position to another, it must pass through the intermediate
rlm@533:    positions. The path invariably forms loops as common actions are
rlm@533:    repeated. Finally and most importantly, proprioception alone
rlm@533:    actually gives very strong inference about the other senses. For
rlm@533:    example, when the worm is proprioceptively flat over several
rlm@533:    frames, you can infer that it is touching the ground and that its
rlm@451:    muscles are not active, because if the muscles were active, the
rlm@533:    worm would be moving and would not remain perfectly flat. In order
rlm@533:    to stay flat, the worm has to be touching the ground, or it would
rlm@451:    again be moving out of the flat position due to gravity. If the
rlm@451:    worm is positioned in such a way that it interacts with itself,
rlm@451:    then it is very likely to be feeling the same tactile feelings as
rlm@451:    the last time it was in that position, because it has the same body
rlm@533:    as then. As you observe multiple frames of proprioceptive data, you
rlm@533:    can become increasingly confident about the exact activations of
rlm@533:    the worm's muscles, because it generally takes a unique combination
rlm@533:    of muscle contractions to transform the worm's body along a
rlm@533:    specific path through \Phi-space.
rlm@533: 
rlm@533:    The worm's total life experience is a long looping path through
rlm@533:    \Phi-space. I will now introduce simple way of taking that
rlm@548:    experience path and building a function that can infer complete
rlm@533:    sensory experience given only a stream of proprioceptive data. This
rlm@533:    /empathy/ function will provide a bridge to use the body centered
rlm@533:    action predicates on video-like streams of information.
rlm@533:    
rlm@535: ** Empathy is the process of building paths in \Phi-space
rlm@449: 
rlm@450:    Here is the core of a basic empathy algorithm, starting with an
rlm@451:    experience vector:
rlm@451: 
rlm@533:    An /experience-index/ is an index into the grand experience vector
rlm@534:    that defines the worm's life. It is a time-stamp for each set of
rlm@533:    sensations the worm has experienced.
rlm@533: 
rlm@562:    First, I group the experience-indices into bins according to the
rlm@533:    similarity of their proprioceptive data. I organize my bins into a
rlm@534:    3 level hierarchy. The smallest bins have an approximate size of
rlm@533:    0.001 radians in all proprioceptive dimensions. Each higher level
rlm@533:    is 10x bigger than the level below it.
rlm@533: 
rlm@533:    The bins serve as a hashing function for proprioceptive data. Given
rlm@562:    a single piece of proprioceptive experience, the bins allow me to
rlm@534:    rapidly find all other similar experience-indices of past
rlm@534:    experience that had a very similar proprioceptive configuration.
rlm@533:    When looking up a proprioceptive experience, if the smallest bin
rlm@562:    does not match any previous experience, then I use successively
rlm@562:    larger bins until a match is found or I reach the largest bin.
rlm@450:    
rlm@533:    Given a sequence of proprioceptive input, I use the bins to
rlm@534:    generate a set of similar experiences for each input using the
rlm@533:    tiered proprioceptive bins.
rlm@533: 
rlm@533:    Finally, to infer sensory data, I select the longest consecutive
rlm@534:    chain of experiences that threads through the sets of similar
rlm@535:    experiences, starting with the current moment as a root and going
rlm@535:    backwards. Consecutive experience means that the experiences appear
rlm@535:    next to each other in the experience vector.
rlm@535: 
rlm@535:    A stream of proprioceptive input might be:
rlm@533:    
rlm@535:    #+BEGIN_EXAMPLE
rlm@535:    [ flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535:    #+END_EXAMPLE
rlm@535: 
rlm@535:    The worm's previous experience of lying on the ground and lifting
rlm@547:    its head generates possible interpretations for each frame (the
rlm@547:    numbers are experience-indices):
rlm@535: 
rlm@567:    \clearpage
rlm@567: 
rlm@535:    #+BEGIN_EXAMPLE
rlm@535:    [ flat, flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535:       1     1     1     1     1     1     1     4     
rlm@535:       2     2     2     2     2     2     2   
rlm@535:       3     3     3     3     3     3     3
rlm@554:       6     6     6     6     6     6     6
rlm@535:       7     7     7     7     7     7     7
rlm@535:       8     8     8     8     8     8     8
rlm@535:       9     9     9     9     9     9     9
rlm@535:    #+END_EXAMPLE
rlm@535:    
rlm@535:    These interpretations suggest a new path through phi space:
rlm@535:    
rlm@535:    #+BEGIN_EXAMPLE
rlm@535:    [ flat, flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535:       6     7     8     9     1     2     3     4
rlm@535:    #+END_EXAMPLE
rlm@535: 
rlm@535:    The new path through \Phi-space is synthesized from two actual
rlm@547:    paths that the creature has experienced: the "1-2-3-4" chain and
rlm@547:    the "6-7-8-9" chain. The "1-2-3-4" chain is necessary because it
rlm@547:    ends with the worm lifting its head. It originated from a short
rlm@535:    training session where the worm rested on the floor for a brief
rlm@535:    while and then raised its head. The "6-7-8-9" chain is part of a
rlm@535:    longer chain of inactivity where the worm simply rested on the
rlm@535:    floor without moving. It is preferred over a "1-2-3" chain (which
rlm@535:    also describes inactivity) because it is longer. The main ideas
rlm@535:    again:
rlm@535:    
rlm@535:    - Imagined \Phi-space paths are synthesized by looping and mixing
rlm@535:      previous experiences.
rlm@535: 
rlm@535:    - Longer experience paths (less edits) are preferred.
rlm@535: 
rlm@535:    - The present is more important than the past --- more recent
rlm@535:      events take precedence in interpretation.
rlm@533: 
rlm@450:    This algorithm has three advantages: 
rlm@450: 
rlm@450:    1. It's simple
rlm@450: 
rlm@451:    3. It's very fast -- retrieving possible interpretations takes
rlm@451:       constant time. Tracing through chains of interpretations takes
rlm@451:       time proportional to the average number of experiences in a
rlm@451:       proprioceptive bin. Redundant experiences in \Phi-space can be
rlm@451:       merged to save computation.
rlm@450: 
rlm@450:    2. It protects from wrong interpretations of transient ambiguous
rlm@451:       proprioceptive data. For example, if the worm is flat for just
rlm@517:       an instant, this flatness will not be interpreted as implying
rlm@517:       that the worm has its muscles relaxed, since the flatness is
rlm@450:       part of a longer chain which includes a distinct pattern of
rlm@451:       muscle activation. Markov chains or other memoryless statistical
rlm@451:       models that operate on individual frames may very well make this
rlm@451:       mistake.
rlm@450: 
rlm@450:    #+caption: Program to convert an experience vector into a 
rlm@450:    #+caption: proprioceptively binned lookup function.
rlm@450:    #+name: bin
rlm@452: #+begin_listing clojure
rlm@450:    #+begin_src clojure
rlm@449: (defn bin [digits]
rlm@449:   (fn [angles]
rlm@449:     (->> angles
rlm@449:          (flatten)
rlm@449:          (map (juxt #(Math/sin %) #(Math/cos %)))
rlm@449:          (flatten)
rlm@449:          (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))
rlm@449: 
rlm@449: (defn gen-phi-scan 
rlm@450:   "Nearest-neighbors with binning. Only returns a result if
rlm@517:    the proprioceptive data is within 10% of a previously recorded
rlm@450:    result in all dimensions."
rlm@450:   [phi-space]
rlm@449:   (let [bin-keys (map bin [3 2 1])
rlm@449:         bin-maps
rlm@449:         (map (fn [bin-key]
rlm@449:                (group-by
rlm@449:                 (comp bin-key :proprioception phi-space)
rlm@449:                 (range (count phi-space)))) bin-keys)
rlm@449:         lookups (map (fn [bin-key bin-map]
rlm@450:                        (fn [proprio] (bin-map (bin-key proprio))))
rlm@450:                      bin-keys bin-maps)]
rlm@449:     (fn lookup [proprio-data]
rlm@449:       (set (some #(% proprio-data) lookups)))))
rlm@450:    #+end_src
rlm@450:    #+end_listing
rlm@449: 
rlm@451:    #+caption: =longest-thread= finds the longest path of consecutive 
rlm@536:    #+caption: past experiences to explain proprioceptive worm data from 
rlm@520:    #+caption: previous data. Here, the film strip represents the  
rlm@531:    #+caption: creature's previous experience. Sort sequences of
rlm@520:    #+caption: memories are spliced together to match the
rlm@520:    #+caption: proprioceptive data. Their carry the other senses 
rlm@520:    #+caption: along with them.
rlm@451:    #+name: phi-space-history-scan
rlm@451:    #+ATTR_LaTeX: :width 10cm
rlm@520:    [[./images/film-of-imagination.png]]
rlm@451: 
rlm@451:    =longest-thread= infers sensory data by stitching together pieces
rlm@451:    from previous experience. It prefers longer chains of previous
rlm@451:    experience to shorter ones. For example, during training the worm
rlm@451:    might rest on the ground for one second before it performs its
rlm@517:    exercises. If during recognition the worm rests on the ground for
rlm@517:    five seconds, =longest-thread= will accommodate this five second
rlm@451:    rest period by looping the one second rest chain five times.
rlm@451: 
rlm@517:    =longest-thread= takes time proportional to the average number of
rlm@451:    entries in a proprioceptive bin, because for each element in the
rlm@517:    starting bin it performs a series of set lookups in the preceding
rlm@536:    bins. If the total history is limited, then this takes time
rlm@548:    proportional to a only a constant multiple of the number of entries
rlm@536:    in the starting bin. This analysis also applies, even if the action
rlm@536:    requires multiple longest chains -- it's still the average number
rlm@536:    of entries in a proprioceptive bin times the desired chain length.
rlm@536:    Because =longest-thread= is so efficient and simple, I can
rlm@536:    interpret worm-actions in real time.
rlm@449: 
rlm@450:    #+caption: Program to calculate empathy by tracing though \Phi-space
rlm@450:    #+caption: and finding the longest (ie. most coherent) interpretation
rlm@450:    #+caption: of the data.
rlm@450:    #+name: longest-thread
rlm@452: #+begin_listing clojure
rlm@450:    #+begin_src clojure
rlm@449: (defn longest-thread
rlm@449:   "Find the longest thread from phi-index-sets. The index sets should
rlm@449:    be ordered from most recent to least recent."
rlm@449:   [phi-index-sets]
rlm@449:   (loop [result '()
rlm@449:          [thread-bases & remaining :as phi-index-sets] phi-index-sets]
rlm@449:     (if (empty? phi-index-sets)
rlm@449:       (vec result)
rlm@449:       (let [threads
rlm@449:             (for [thread-base thread-bases]
rlm@449:               (loop [thread (list thread-base)
rlm@449:                      remaining remaining]
rlm@449:                 (let [next-index (dec (first thread))]
rlm@449:                   (cond (empty? remaining) thread
rlm@449:                         (contains? (first remaining) next-index)
rlm@449:                         (recur
rlm@449:                          (cons next-index thread) (rest remaining))
rlm@449:                         :else thread))))
rlm@449:             longest-thread
rlm@449:             (reduce (fn [thread-a thread-b]
rlm@449:                       (if (> (count thread-a) (count thread-b))
rlm@449:                         thread-a thread-b))
rlm@449:                     '(nil)
rlm@449:                     threads)]
rlm@449:         (recur (concat longest-thread result)
rlm@449:                (drop (count longest-thread) phi-index-sets))))))
rlm@450:    #+end_src
rlm@450:    #+end_listing
rlm@450: 
rlm@451:    There is one final piece, which is to replace missing sensory data
rlm@451:    with a best-guess estimate. While I could fill in missing data by
rlm@451:    using a gradient over the closest known sensory data points,
rlm@451:    averages can be misleading. It is certainly possible to create an
rlm@451:    impossible sensory state by averaging two possible sensory states.
rlm@536:    For example, consider moving your hand in an arc over your head. If
rlm@536:    for some reason you only have the initial and final positions of
rlm@536:    this movement in your \Phi-space, averaging them together will
rlm@536:    produce the proprioceptive sensation of having your hand /inside/
rlm@536:    your head, which is physically impossible to ever experience
rlm@536:    (barring motor adaption illusions). Therefore I simply replicate
rlm@536:    the most recent sensory experience to fill in the gaps.
rlm@449: 
rlm@449:    #+caption: Fill in blanks in sensory experience by replicating the most 
rlm@449:    #+caption: recent experience.
rlm@449:    #+name: infer-nils
rlm@452: #+begin_listing clojure
rlm@449:    #+begin_src clojure
rlm@449: (defn infer-nils
rlm@449:   "Replace nils with the next available non-nil element in the
rlm@449:    sequence, or barring that, 0."
rlm@449:   [s]
rlm@449:   (loop [i (dec (count s))
rlm@449:          v (transient s)]
rlm@449:     (if (zero? i) (persistent! v)
rlm@449:         (if-let [cur (v i)]
rlm@449:           (if (get v (dec i) 0)
rlm@449:             (recur (dec i) v)
rlm@449:             (recur (dec i) (assoc! v (dec i) cur)))
rlm@449:           (recur i (assoc! v i 0))))))
rlm@449:    #+end_src
rlm@449:    #+end_listing
rlm@435:   
rlm@541: ** =EMPATH= recognizes actions efficiently
rlm@451:    
rlm@451:    To use =EMPATH= with the worm, I first need to gather a set of
rlm@451:    experiences from the worm that includes the actions I want to
rlm@452:    recognize. The =generate-phi-space= program (listing
rlm@451:    \ref{generate-phi-space} runs the worm through a series of
rlm@545:    exercises and gathers those experiences into a vector. The
rlm@451:    =do-all-the-things= program is a routine expressed in a simple
rlm@452:    muscle contraction script language for automated worm control. It
rlm@452:    causes the worm to rest, curl, and wiggle over about 700 frames
rlm@452:    (approx. 11 seconds).
rlm@425: 
rlm@451:    #+caption: Program to gather the worm's experiences into a vector for 
rlm@451:    #+caption: further processing. The =motor-control-program= line uses
rlm@451:    #+caption: a motor control script that causes the worm to execute a series
rlm@517:    #+caption: of ``exercises'' that include all the action predicates.
rlm@451:    #+name: generate-phi-space
rlm@452: #+begin_listing clojure 
rlm@451:    #+begin_src clojure
rlm@451: (def do-all-the-things 
rlm@451:   (concat
rlm@451:    curl-script
rlm@451:    [[300 :d-ex 40]
rlm@451:     [320 :d-ex 0]]
rlm@451:    (shift-script 280 (take 16 wiggle-script))))
rlm@451: 
rlm@451: (defn generate-phi-space []
rlm@451:   (let [experiences (atom [])]
rlm@451:     (run-world
rlm@451:      (apply-map 
rlm@451:       worm-world
rlm@451:       (merge
rlm@451:        (worm-world-defaults)
rlm@451:        {:end-frame 700
rlm@451:         :motor-control
rlm@451:         (motor-control-program worm-muscle-labels do-all-the-things)
rlm@451:         :experiences experiences})))
rlm@451:     @experiences))
rlm@451:    #+end_src
rlm@451:    #+end_listing
rlm@451: 
rlm@536:    #+caption: Use =longest-thread= and a \Phi-space generated from a short
rlm@451:    #+caption: exercise routine to interpret actions during free play.
rlm@451:    #+name: empathy-debug
rlm@452: #+begin_listing clojure
rlm@451:    #+begin_src clojure
rlm@451: (defn init []
rlm@451:   (def phi-space (generate-phi-space))
rlm@451:   (def phi-scan (gen-phi-scan phi-space)))
rlm@451: 
rlm@451: (defn empathy-demonstration []
rlm@451:   (let [proprio (atom ())]
rlm@451:     (fn
rlm@451:       [experiences text]
rlm@451:       (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451:         (swap! proprio (partial cons phi-indices))
rlm@451:         (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451:               empathy (mapv phi-space (infer-nils exp-thread))]
rlm@451:           (println-repl (vector:last-n exp-thread 22))
rlm@451:           (cond
rlm@451:            (grand-circle? empathy) (.setText text "Grand Circle")
rlm@451:            (curled? empathy)       (.setText text "Curled")
rlm@451:            (wiggling? empathy)     (.setText text "Wiggling")
rlm@451:            (resting? empathy)      (.setText text "Resting")
rlm@536:            :else                   (.setText text "Unknown")))))))
rlm@451: 
rlm@451: (defn empathy-experiment [record]
rlm@451:   (.start (worm-world :experience-watch (debug-experience-phi)
rlm@451:                       :record record :worm worm*)))
rlm@451:    #+end_src
rlm@451:    #+end_listing
rlm@536: 
rlm@536:    These programs create a test for the empathy system. First, the
rlm@536:    worm's \Phi-space is generated from a simple motor script. Then the
rlm@536:    worm is re-created in an environment almost exactly identical to
rlm@536:    the testing environment for the action-predicates, with one major
rlm@536:    difference : the only sensory information available to the system
rlm@536:    is proprioception. From just the proprioception data and
rlm@548:    \Phi-space, =longest-thread= synthesizes a complete record the last
rlm@536:    300 sensory experiences of the worm. These synthesized experiences
rlm@536:    are fed directly into the action predicates =grand-circle?=,
rlm@553:    =curled?=, =wiggling?=, and =resting?= and their outputs are
rlm@553:    printed to the screen at each frame.
rlm@451:    
rlm@451:    The result of running =empathy-experiment= is that the system is
rlm@451:    generally able to interpret worm actions using the action-predicates
rlm@451:    on simulated sensory data just as well as with actual data. Figure
rlm@451:    \ref{empathy-debug-image} was generated using =empathy-experiment=:
rlm@451: 
rlm@451:   #+caption: From only proprioceptive data, =EMPATH= was able to infer 
rlm@451:   #+caption: the complete sensory experience and classify four poses
rlm@517:   #+caption: (The last panel shows a composite image of /wiggling/, 
rlm@451:   #+caption: a dynamic pose.)
rlm@451:   #+name: empathy-debug-image
rlm@567:   #+ATTR_LaTeX: :width 10cm
rlm@451:   [[./images/empathy-1.png]]
rlm@451: 
rlm@451:   One way to measure the performance of =EMPATH= is to compare the
rlm@517:   suitability of the imagined sense experience to trigger the same
rlm@451:   action predicates as the real sensory experience. 
rlm@451:   
rlm@451:    #+caption: Determine how closely empathy approximates actual 
rlm@451:    #+caption: sensory data.
rlm@451:    #+name: test-empathy-accuracy
rlm@452: #+begin_listing clojure
rlm@451:    #+begin_src clojure
rlm@451: (def worm-action-label
rlm@451:   (juxt grand-circle? curled? wiggling?))
rlm@451: 
rlm@451: (defn compare-empathy-with-baseline [matches]
rlm@451:   (let [proprio (atom ())]
rlm@451:     (fn
rlm@451:       [experiences text]
rlm@451:       (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451:         (swap! proprio (partial cons phi-indices))
rlm@451:         (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451:               empathy (mapv phi-space (infer-nils exp-thread))
rlm@451:               experience-matches-empathy
rlm@451:               (= (worm-action-label experiences)
rlm@451:                  (worm-action-label empathy))]
rlm@451:           (println-repl experience-matches-empathy)
rlm@451:           (swap! matches #(conj % experience-matches-empathy)))))))
rlm@451:               
rlm@451: (defn accuracy [v]
rlm@451:   (float (/ (count (filter true? v)) (count v))))
rlm@451: 
rlm@451: (defn test-empathy-accuracy []
rlm@451:   (let [res (atom [])]
rlm@451:     (run-world
rlm@451:      (worm-world :experience-watch
rlm@451:                  (compare-empathy-with-baseline res)
rlm@451:                  :worm worm*))
rlm@451:     (accuracy @res)))
rlm@451:    #+end_src
rlm@451:    #+end_listing
rlm@451: 
rlm@451:   Running =test-empathy-accuracy= using the very short exercise
rlm@555:   program =do-all-the-things= defined in listing
rlm@555:   \ref{generate-phi-space}, and then doing a similar pattern of
rlm@555:   activity using manual control of the worm, yields an accuracy of
rlm@555:   around 73%. This is based on very limited worm experience, and
rlm@555:   almost all errors are due to the worm's \Phi-space being too
rlm@555:   incomplete to properly interpret common poses. By manually training
rlm@555:   the worm for longer using =init-interactive= defined in listing
rlm@555:   \ref{manual-phi-space}, the accuracy dramatically improves:
rlm@451: 
rlm@451:    #+caption: Program to generate \Phi-space using manual training.
rlm@451:    #+name: manual-phi-space
rlm@451:    #+begin_listing clojure
rlm@451:    #+begin_src clojure
rlm@451: (defn init-interactive []
rlm@451:   (def phi-space
rlm@451:     (let [experiences (atom [])]
rlm@451:       (run-world
rlm@451:        (apply-map 
rlm@451:         worm-world
rlm@451:         (merge
rlm@451:          (worm-world-defaults)
rlm@451:          {:experiences experiences})))
rlm@451:       @experiences))
rlm@451:   (def phi-scan (gen-phi-scan phi-space)))
rlm@451:    #+end_src
rlm@451:    #+end_listing
rlm@451: 
rlm@555:   =init-interactive= allows me to take direct control of the worm's
rlm@555:   muscles and run it through each characteristic movement I care
rlm@555:   about. After about 1 minute of manual training, I was able to
rlm@555:   achieve 95% accuracy on manual testing of the worm using
rlm@549:   =test-empathy-accuracy=. The majority of disagreements are near the
rlm@555:   transition boundaries from one type of action to another. During
rlm@549:   these transitions the exact label for the action is often unclear,
rlm@549:   and disagreement between empathy and experience is practically
rlm@549:   irrelevant. Thus, the system's effective identification accuracy is
rlm@549:   even higher than 95%. When I watch this system myself, I generally
rlm@549:   see no errors in action identification compared to my own judgment
rlm@549:   of what the worm is doing.
rlm@536: 
rlm@541: ** Digression: Learning touch sensor layout through free play
rlm@514: 
rlm@551:    In the previous chapter I showed how to compute actions in terms of
rlm@539:    body-centered predicates, but some of those predicates relied on
rlm@539:    the average touch activation of pre-defined regions of the worm's
rlm@539:    skin. What if, instead of receiving touch pre-grouped into the six
rlm@555:    faces of each worm segment, the true partitioning of the worm's
rlm@555:    skin was unknown? This is more similar to how a nerve fiber bundle
rlm@555:    might be arranged inside an animal. While two fibers that are close
rlm@555:    in a nerve bundle /might/ correspond to two touch sensors that are
rlm@555:    close together on the skin, the process of taking a complicated
rlm@555:    surface and forcing it into essentially a 2D circle requires that
rlm@555:    some regions of skin that are close together in the animal end up
rlm@555:    far apart in the nerve bundle.
rlm@452:    
rlm@555:    In this chapter I show how to automatically learn the
rlm@555:    skin-partitioning of a worm segment by free exploration. As the
rlm@555:    worm rolls around on the floor, large sections of its surface get
rlm@555:    activated. If the worm has stopped moving, then whatever region of
rlm@555:    skin that is touching the floor is probably an important region,
rlm@555:    and should be recorded. The code I provide relies on the worm
rlm@555:    segment having flat faces, but still demonstrates a primitive kind
rlm@555:    of multi-sensory bootstrapping that I find appealing.
rlm@555: 
rlm@452:    #+caption: Program to detect whether the worm is in a resting state 
rlm@452:    #+caption: with one face touching the floor.
rlm@452:    #+name: pure-touch
rlm@452:    #+begin_listing clojure
rlm@452:    #+begin_src clojure
rlm@452: (def full-contact [(float 0.0) (float 0.1)])
rlm@452: 
rlm@452: (defn pure-touch?
rlm@452:   "This is worm specific code to determine if a large region of touch
rlm@452:    sensors is either all on or all off."
rlm@452:   [[coords touch :as touch-data]]
rlm@452:   (= (set (map first touch)) (set full-contact)))
rlm@452:    #+end_src
rlm@452:    #+end_listing
rlm@452: 
rlm@452:    After collecting these important regions, there will many nearly
rlm@517:    similar touch regions. While for some purposes the subtle
rlm@452:    differences between these regions will be important, for my
rlm@517:    purposes I collapse them into mostly non-overlapping sets using
rlm@517:    =remove-similar= in listing \ref{remove-similar}
rlm@517: 
rlm@517:    #+caption: Program to take a list of sets of points and ``collapse them''
rlm@517:    #+caption: so that the remaining sets in the list are significantly 
rlm@452:    #+caption: different from each other. Prefer smaller sets to larger ones.
rlm@517:    #+name: remove-similar
rlm@452:    #+begin_listing clojure
rlm@452:    #+begin_src clojure
rlm@452: (defn remove-similar
rlm@452:   [coll]
rlm@452:   (loop [result () coll (sort-by (comp - count) coll)]
rlm@452:     (if (empty? coll) result
rlm@452:         (let  [[x & xs] coll
rlm@452:                c (count x)]
rlm@452:           (if (some
rlm@452:                (fn [other-set]
rlm@452:                  (let [oc (count other-set)]
rlm@452:                    (< (- (count (union other-set x)) c) (* oc 0.1))))
rlm@452:                xs)
rlm@452:             (recur result xs)
rlm@452:             (recur (cons x result) xs))))))
rlm@452:    #+end_src
rlm@452:    #+end_listing
rlm@452: 
rlm@452:    Actually running this simulation is easy given =CORTEX='s facilities.
rlm@452: 
rlm@452:    #+caption: Collect experiences while the worm moves around. Filter the touch 
rlm@517:    #+caption: sensations by stable ones, collapse similar ones together, 
rlm@452:    #+caption: and report the regions learned.
rlm@452:    #+name: learn-touch
rlm@452:    #+begin_listing clojure
rlm@452:    #+begin_src clojure
rlm@452: (defn learn-touch-regions []
rlm@452:   (let [experiences (atom [])
rlm@452:         world (apply-map
rlm@452:                worm-world
rlm@452:                (assoc (worm-segment-defaults)
rlm@452:                  :experiences experiences))]
rlm@452:     (run-world world)
rlm@452:     (->>
rlm@452:      @experiences
rlm@452:      (drop 175)
rlm@452:      ;; access the single segment's touch data
rlm@452:      (map (comp first :touch))
rlm@452:      ;; only deal with "pure" touch data to determine surfaces
rlm@452:      (filter pure-touch?)
rlm@452:      ;; associate coordinates with touch values
rlm@452:      (map (partial apply zipmap))
rlm@452:      ;; select those regions where contact is being made
rlm@452:      (map (partial group-by second))
rlm@452:      (map #(get % full-contact))
rlm@452:      (map (partial map first))
rlm@452:      ;; remove redundant/subset regions
rlm@452:      (map set)
rlm@452:      remove-similar)))
rlm@452: 
rlm@452: (defn learn-and-view-touch-regions []
rlm@452:   (map view-touch-region
rlm@452:        (learn-touch-regions)))
rlm@452:    #+end_src
rlm@452:    #+end_listing
rlm@452: 
rlm@517:    The only thing remaining to define is the particular motion the worm
rlm@452:    must take. I accomplish this with a simple motor control program.
rlm@452: 
rlm@452:    #+caption: Motor control program for making the worm roll on the ground.
rlm@452:    #+caption: This could also be replaced with random motion.
rlm@452:    #+name: worm-roll
rlm@452:    #+begin_listing clojure
rlm@452:    #+begin_src clojure
rlm@452: (defn touch-kinesthetics []
rlm@452:   [[170 :lift-1 40]
rlm@452:    [190 :lift-1 19]
rlm@452:    [206 :lift-1  0]
rlm@452: 
rlm@452:    [400 :lift-2 40]
rlm@452:    [410 :lift-2  0]
rlm@452: 
rlm@452:    [570 :lift-2 40]
rlm@452:    [590 :lift-2 21]
rlm@452:    [606 :lift-2  0]
rlm@452: 
rlm@452:    [800 :lift-1 30]
rlm@452:    [809 :lift-1 0]
rlm@452: 
rlm@452:    [900 :roll-2 40]
rlm@452:    [905 :roll-2 20]
rlm@452:    [910 :roll-2  0]
rlm@452: 
rlm@452:    [1000 :roll-2 40]
rlm@452:    [1005 :roll-2 20]
rlm@452:    [1010 :roll-2  0]
rlm@452:    
rlm@452:    [1100 :roll-2 40]
rlm@452:    [1105 :roll-2 20]
rlm@452:    [1110 :roll-2  0]
rlm@452:    ])
rlm@452:    #+end_src
rlm@452:    #+end_listing
rlm@452: 
rlm@452: 
rlm@452:    #+caption: The small worm rolls around on the floor, driven
rlm@452:    #+caption: by the motor control program in listing \ref{worm-roll}.
rlm@452:    #+name: worm-roll
rlm@452:    #+ATTR_LaTeX: :width 12cm
rlm@452:    [[./images/worm-roll.png]]
rlm@452: 
rlm@452:    #+caption: After completing its adventures, the worm now knows 
rlm@548:    #+caption: how its touch sensors are arranged along its skin. Each of these six rectangles are touch sensory patterns that were 
rlm@548:    #+caption: deemed important by 
rlm@537:    #+caption: =learn-touch-regions=. Each white square in the rectangles 
rlm@537:    #+caption: above is a cluster of ``related" touch nodes as determined 
rlm@548:    #+caption: by the system. The worm has correctly discovered that it has six faces, and has partitioned its sensory map into these six faces. 
rlm@452:    #+name: worm-touch-map
rlm@452:    #+ATTR_LaTeX: :width 12cm
rlm@452:    [[./images/touch-learn.png]]
rlm@452: 
rlm@452:    While simple, =learn-touch-regions= exploits regularities in both
rlm@452:    the worm's physiology and the worm's environment to correctly
rlm@452:    deduce that the worm has six sides. Note that =learn-touch-regions=
rlm@452:    would work just as well even if the worm's touch sense data were
rlm@517:    completely scrambled. The cross shape is just for convenience. This
rlm@452:    example justifies the use of pre-defined touch regions in =EMPATH=.
rlm@452: 
rlm@548: ** Recognizing an object using embodied representation
rlm@548: 
rlm@548:    At the beginning of the thesis, I suggested that we might recognize
rlm@548:    the chair in Figure \ref{hidden-chair} by imagining ourselves in
rlm@548:    the position of the man and realizing that he must be sitting on
rlm@548:    something in order to maintain that position. Here, I present a
rlm@548:    brief elaboration on how to this might be done.
rlm@548: 
rlm@548:    First, I need the feeling of leaning or resting /on/ some other
rlm@548:    object that is not the floor. This feeling is easy to describe
rlm@548:    using an embodied representation. 
rlm@548: 
rlm@548:   #+caption: Program describing the sense of leaning or resting on something.
rlm@548:   #+caption: This involves a relaxed posture, the feeling of touching something, 
rlm@548:   #+caption: and a period of stability where the worm does not move.
rlm@548:   #+name: draped
rlm@548:   #+begin_listing clojure
rlm@548:   #+begin_src clojure
rlm@548: (defn draped?
rlm@548:   "Is the worm:
rlm@548:     -- not flat (the floor is not a 'chair')
rlm@548:     -- supported (not using its muscles to hold its position)
rlm@548:     -- stable (not changing its position)
rlm@548:     -- touching something (must register contact)"
rlm@548:   [experiences]
rlm@548:   (let [b2-hash (bin 2)
rlm@548:         touch (:touch (peek experiences))
rlm@548:         total-contact
rlm@548:         (reduce
rlm@548:          +
rlm@548:          (map #(contact all-touch-coordinates %)
rlm@548:               (rest touch)))]
rlm@548:     (println total-contact)
rlm@548:     (and (not (resting? experiences))
rlm@548:          (every?
rlm@548:           zero?
rlm@548:           (-> experiences
rlm@548:               (vector:last-n 25)
rlm@548:               (#(map :muscle %))
rlm@548:               (flatten)))
rlm@548:          (-> experiences
rlm@548:              (vector:last-n 20)
rlm@548:              (#(map (comp b2-hash flatten :proprioception) %))
rlm@548:              (set)
rlm@548:              (count) (= 1))
rlm@548:          (< 0.03 total-contact))))
rlm@548:   #+end_src
rlm@548:   #+end_listing
rlm@548:    
rlm@553:    #+caption: The =draped?= predicate detects the presence of the cube
rlm@553:    #+caption: whenever the worm interacts with it. The details of the
rlm@553:    #+caption: cube are irrelevant; only the way it influences the
rlm@553:    #+caption: worm's body matters. The ``unknown'' label on the fifth
rlm@553:    #+caption: frame is due to the fact that the worm is not
rlm@556:    #+caption: stationary. =draped?= will only declare that the worm
rlm@553:    #+caption: is draped if it has been still for a while.
rlm@548:    #+name: draped-video
rlm@548:    #+ATTR_LaTeX: :width 13cm
rlm@548:    [[./images/draped.png]]
rlm@548: 
rlm@548:    Though this is a simple example, using the =draped?= predicate to
rlm@550:    detect a cube has interesting advantages. The =draped?= predicate
rlm@548:    describes the cube not in terms of properties that the cube has,
rlm@548:    but instead in terms of how the worm interacts with it physically.
rlm@548:    This means that the cube can still be detected even if it is not
rlm@548:    visible, as long as its influence on the worm's body is visible.
rlm@548:    
rlm@548:    This system will also see the virtual cube created by a
rlm@548:    ``mimeworm", which uses its muscles in a very controlled way to
rlm@548:    mimic the appearance of leaning on a cube. The system will
rlm@548:    anticipate that there is an actual invisible cube that provides
rlm@548:    support!
rlm@548: 
rlm@548:    #+caption: Can you see the thing that this person is leaning on?
rlm@553:    #+caption: What properties does it have, other than how it makes
rlm@553:    #+caption: the man's elbow and shoulder feel? I wonder if people
rlm@553:    #+caption: who can actually maintain this pose easily still see the
rlm@553:    #+caption: support?
rlm@548:    #+name: mime
rlm@548:    #+ATTR_LaTeX: :width 6cm
rlm@548:    [[./images/pablo-the-mime.png]]
rlm@548: 
rlm@548:    This makes me wonder about the psychology of actual mimes. Suppose
rlm@548:    for a moment that people have something analogous to \Phi-space and
rlm@548:    that one of the ways that they find objects in a scene is by their
rlm@548:    relation to other people's bodies. Suppose that a person watches a
rlm@548:    person miming an invisible wall. For a person with no experience
rlm@548:    with miming, their \Phi-space will only have entries that describe
rlm@548:    the scene with the sensation of their hands touching a wall. This
rlm@548:    sensation of touch will create a strong impression of a wall, even
rlm@548:    though the wall would have to be invisible. A person with
rlm@548:    experience in miming however, will have entries in their \Phi-space
rlm@548:    that describe the wall-miming position without a sense of touch. It
rlm@548:    will not seem to such as person that an invisible wall is present,
rlm@548:    but merely that the mime is holding out their hands in a special
rlm@548:    way. Thus, the theory that humans use something like \Phi-space
rlm@548:    weakly predicts that learning how to mime should break the power of
rlm@548:    miming illusions. Most optical illusions still work no matter how
rlm@548:    much you know about them, so this proposal would be quite
rlm@548:    interesting to test, as it predicts a non-standard result!
rlm@548: 
rlm@548: 
rlm@548: #+BEGIN_LaTeX
rlm@548: \clearpage
rlm@548: #+END_LaTeX
rlm@548: 
rlm@558: * Contributions
rlm@548: 
rlm@548:   The big idea behind this thesis is a new way to represent and
rlm@548:   recognize physical actions, which I call /empathic representation/.
rlm@548:   Actions are represented as predicates which have access to the
rlm@548:   totality of a creature's sensory abilities. To recognize the
rlm@548:   physical actions of another creature similar to yourself, you
rlm@548:   imagine what they would feel by examining the position of their body
rlm@548:   and relating it to your own previous experience.
rlm@454:   
rlm@548:   Empathic representation of physical actions is robust and general.
rlm@548:   Because the representation is body-centered, it avoids baking in a
rlm@548:   particular viewpoint like you might get from learning from example
rlm@548:   videos. Because empathic representation relies on all of a
rlm@542:   creature's senses, it can describe exactly what an action /feels
rlm@542:   like/ without getting caught up in irrelevant details such as visual
rlm@542:   appearance. I think it is important that a correct description of
rlm@548:   jumping (for example) should not include irrelevant details such as
rlm@548:   the color of a person's clothes or skin; empathic representation can
rlm@548:   get right to the heart of what jumping is by describing it in terms
rlm@548:   of touch, muscle contractions, and a brief feeling of
rlm@548:   weightlessness. Empathic representation is very low-level in that it
rlm@548:   describes actions using concrete sensory data with little
rlm@548:   abstraction, but it has the generality of much more abstract
rlm@548:   representations!
rlm@542: 
rlm@542:   Another important contribution of this thesis is the development of
rlm@542:   the =CORTEX= system, a complete environment for creating simulated
rlm@542:   creatures. You have seen how to implement five senses: touch,
rlm@542:   proprioception, hearing, vision, and muscle tension. You have seen
rlm@562:   how to create new creatures using Blender, a 3D modeling tool.
rlm@542: 
rlm@461:   As a minor digression, you also saw how I used =CORTEX= to enable a
rlm@461:   tiny worm to discover the topology of its skin simply by rolling on
rlm@548:   the ground.  You also saw how to detect objects using only embodied
rlm@548:   predicates. 
rlm@548: 
rlm@548:   In conclusion, for this thesis I:
rlm@548: 
rlm@548:    - Developed the idea of embodied representation, which describes
rlm@548:      actions that a creature can do in terms of first-person sensory
rlm@548:      data. 
rlm@548: 
rlm@548:    - Developed a method of empathic action recognition which uses
rlm@548:      previous embodied experience and embodied representation of
rlm@548:      actions to greatly constrain the possible interpretations of an
rlm@548:      action.
rlm@548: 
rlm@548:    - Created =EMPATH=, a program which uses empathic action
rlm@548:      recognition to recognize physical actions in a simple model
rlm@548:      involving segmented worm-like creatures.
rlm@548: 
rlm@548:    - Created =CORTEX=, a comprehensive platform for embodied AI
rlm@548:      experiments. It is the base on which =EMPATH= is built.
rlm@517: 
rlm@488: #+BEGIN_LaTeX
rlm@548: \clearpage
rlm@488: \appendix
rlm@488: #+END_LaTeX
rlm@517: 
rlm@558: * Appendix: =CORTEX= User Guide
rlm@488: 
rlm@488:   Those who write a thesis should endeavor to make their code not only
rlm@517:   accessible, but actually usable, as a way to pay back the community
rlm@488:   that made the thesis possible in the first place. This thesis would
rlm@488:   not be possible without Free Software such as jMonkeyEngine3,
rlm@556:   Blender, clojure, =emacs=, =ffmpeg=, and many other tools. That is
rlm@556:   why I have included this user guide, in the hope that someone else
rlm@556:   might find =CORTEX= useful.
rlm@488: 
rlm@488: ** Obtaining =CORTEX= 
rlm@488: 
rlm@488:    You can get cortex from its mercurial repository at
rlm@488:    http://hg.bortreb.com/cortex. You may also download =CORTEX=
rlm@488:    releases at http://aurellem.org/cortex/releases/. As a condition of
rlm@488:    making this thesis, I have also provided Professor Winston the
rlm@488:    =CORTEX= source, and he knows how to run the demos and get started.
rlm@488:    You may also email me at =cortex@aurellem.org= and I may help where
rlm@488:    I can.
rlm@488: 
rlm@488: ** Running =CORTEX= 
rlm@488:    
rlm@488:    =CORTEX= comes with README and INSTALL files that will guide you
rlm@488:    through installation and running the test suite. In particular you
rlm@488:    should look at test =cortex.test= which contains test suites that
rlm@488:    run through all senses and multiple creatures.
rlm@488: 
rlm@488: ** Creating creatures
rlm@488: 
rlm@488:    Creatures are created using /Blender/, a free 3D modeling program.
rlm@488:    You will need Blender version 2.6 when using the =CORTEX= included
rlm@517:    in this thesis. You create a =CORTEX= creature in a similar manner
rlm@488:    to modeling anything in Blender, except that you also create
rlm@488:    several trees of empty nodes which define the creature's senses.
rlm@488: 
rlm@488: *** Mass 
rlm@488:     
rlm@488:     To give an object mass in =CORTEX=, add a ``mass'' metadata label
rlm@488:     to the object with the mass in jMonkeyEngine units. Note that
rlm@488:     setting the mass to 0 causes the object to be immovable.
rlm@488: 
rlm@488: *** Joints
rlm@488: 
rlm@488:     Joints are created by creating an empty node named =joints= and
rlm@488:     then creating any number of empty child nodes to represent your
rlm@488:     creature's joints. The joint will automatically connect the
rlm@488:     closest two physical objects. It will help to set the empty node's
rlm@488:     display mode to ``Arrows'' so that you can clearly see the
rlm@488:     direction of the axes.
rlm@488:    
rlm@488:     Joint nodes should have the following metadata under the ``joint''
rlm@488:     label:
rlm@488: 
rlm@488:     #+BEGIN_SRC clojure
rlm@540: ;; ONE of the following, under the label "joint":
rlm@488: {:type :point}
rlm@488: 
rlm@488: ;; OR
rlm@488: 
rlm@488: {:type :hinge
rlm@488:  :limit [<limit-low> <limit-high>]
rlm@488:  :axis (Vector3f. <x> <y> <z>)}
rlm@488: ;;(:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)
rlm@488: 
rlm@488: ;; OR
rlm@488: 
rlm@488: {:type :cone
rlm@488:  :limit-xz <lim-xz>
rlm@488:  :limit-xy <lim-xy>
rlm@562:  :twist    <lim-twist>}   ;(use XZY rotation mode in Blender!)
rlm@488:     #+END_SRC
rlm@488: 
rlm@488: *** Eyes
rlm@488: 
rlm@488:     Eyes are created by creating an empty node named =eyes= and then
rlm@488:     creating any number of empty child nodes to represent your
rlm@488:     creature's eyes.
rlm@488: 
rlm@488:     Eye nodes should have the following metadata under the ``eye''
rlm@488:     label:
rlm@488: 
rlm@488: #+BEGIN_SRC clojure
rlm@488: {:red    <red-retina-definition>
rlm@488:  :blue   <blue-retina-definition>
rlm@488:  :green  <green-retina-definition>
rlm@488:  :all    <all-retina-definition>
rlm@488:  (<0xrrggbb> <custom-retina-image>)...
rlm@488: }
rlm@488: #+END_SRC
rlm@488: 
rlm@488:     Any of the color channels may be omitted. You may also include
rlm@488:     your own color selectors, and in fact :red is equivalent to
rlm@488:     0xFF0000 and so forth. The eye will be placed at the same position
rlm@488:     as the empty node and will bind to the neatest physical object.
rlm@488:     The eye will point outward from the X-axis of the node, and ``up''
rlm@488:     will be in the direction of the X-axis of the node. It will help
rlm@488:     to set the empty node's display mode to ``Arrows'' so that you can
rlm@488:     clearly see the direction of the axes.
rlm@488: 
rlm@517:     Each retina file should contain white pixels wherever you want to be
rlm@488:     sensitive to your chosen color. If you want the entire field of
rlm@488:     view, specify :all of 0xFFFFFF and a retinal map that is entirely
rlm@488:     white. 
rlm@488: 
rlm@488:     Here is a sample retinal map:
rlm@488: 
rlm@488:     #+caption: An example retinal profile image. White pixels are 
rlm@488:     #+caption: photo-sensitive elements. The distribution of white 
rlm@488:     #+caption: pixels is denser in the middle and falls off at the 
rlm@488:     #+caption: edges and is inspired by the human retina.
rlm@488:     #+name: retina
rlm@488:     #+ATTR_LaTeX: :width 7cm :placement [H]
rlm@488:     [[./images/retina-small.png]]
rlm@488: 
rlm@488: *** Hearing
rlm@488: 
rlm@488:     Ears are created by creating an empty node named =ears= and then
rlm@488:     creating any number of empty child nodes to represent your
rlm@488:     creature's ears. 
rlm@488: 
rlm@488:     Ear nodes do not require any metadata.
rlm@488: 
rlm@488:     The ear will bind to and follow the closest physical node.
rlm@488: 
rlm@488: *** Touch
rlm@488: 
rlm@488:     Touch is handled similarly to mass. To make a particular object
rlm@488:     touch sensitive, add metadata of the following form under the
rlm@488:     object's ``touch'' metadata field:
rlm@488:     
rlm@488:     #+BEGIN_EXAMPLE
rlm@488:     <touch-UV-map-file-name>    
rlm@488:     #+END_EXAMPLE
rlm@488: 
rlm@488:     You may also include an optional ``scale'' metadata number to
rlm@517:     specify the length of the touch feelers. The default is $0.1$,
rlm@488:     and this is generally sufficient.
rlm@488: 
rlm@488:     The touch UV should contain white pixels for each touch sensor.
rlm@488: 
rlm@488:     Here is an example touch-uv map that approximates a human finger,
rlm@488:     and its corresponding model.
rlm@488: 
rlm@488:     #+caption: This is the tactile-sensor-profile for the upper segment 
rlm@488:     #+caption: of a fingertip. It defines regions of high touch sensitivity 
rlm@488:     #+caption: (where there are many white pixels) and regions of low 
rlm@488:     #+caption: sensitivity (where white pixels are sparse).
rlm@488:     #+name: guide-fingertip-UV
rlm@567:     #+ATTR_LaTeX: :width 9cm 
rlm@488:     [[./images/finger-UV.png]]
rlm@488: 
rlm@488:     #+caption: The fingertip UV-image form above applied to a simple
rlm@488:     #+caption: model of a fingertip.
rlm@488:     #+name: guide-fingertip
rlm@567:     #+ATTR_LaTeX: :width 9cm 
rlm@553:     [[./images/finger-1.png]]
rlm@488: 
rlm@517: *** Proprioception
rlm@488: 
rlm@488:     Proprioception is tied to each joint node -- nothing special must
rlm@562:     be done in a Blender model to enable proprioception other than
rlm@488:     creating joint nodes.
rlm@488: 
rlm@488: *** Muscles
rlm@488: 
rlm@488:     Muscles are created by creating an empty node named =muscles= and
rlm@488:     then creating any number of empty child nodes to represent your
rlm@488:     creature's muscles.
rlm@488: 
rlm@488:     
rlm@488:     Muscle nodes should have the following metadata under the
rlm@488:     ``muscle'' label:
rlm@488: 
rlm@488:     #+BEGIN_EXAMPLE
rlm@488:     <muscle-profile-file-name>
rlm@488:     #+END_EXAMPLE
rlm@488: 
rlm@488:     Muscles should also have a ``strength'' metadata entry describing
rlm@488:     the muscle's total strength at full activation. 
rlm@488: 
rlm@488:     Muscle profiles are simple images that contain the relative amount
rlm@488:     of muscle power in each simulated alpha motor neuron. The width of
rlm@488:     the image is the total size of the motor pool, and the redness of
rlm@488:     each neuron is the relative power of that motor pool.
rlm@488: 
rlm@488:     While the profile image can have any dimensions, only the first
rlm@488:     line of pixels is used to define the muscle. Here is a sample
rlm@488:     muscle profile image that defines a human-like muscle.
rlm@488: 
rlm@488:     #+caption: A muscle profile image that describes the strengths
rlm@488:     #+caption: of each motor neuron in a muscle. White is weakest 
rlm@488:     #+caption: and dark red is strongest. This particular pattern 
rlm@488:     #+caption: has weaker motor neurons at the beginning, just 
rlm@488:     #+caption: like human muscle.
rlm@488:     #+name: muscle-recruit
rlm@567:     #+ATTR_LaTeX: :width 7cm 
rlm@488:     [[./images/basic-muscle.png]]
rlm@488:     
rlm@488:     Muscles twist the nearest physical object about the muscle node's
rlm@488:     Z-axis. I recommend using the ``Single Arrow'' display mode for
rlm@488:     muscles and using the right hand rule to determine which way the
rlm@488:     muscle will twist. To make a segment that can twist in multiple
rlm@488:     directions, create multiple, differently aligned muscles.
rlm@488: 
rlm@488: ** =CORTEX= API
rlm@488: 
rlm@488:    These are the some functions exposed by =CORTEX= for creating
rlm@488:    worlds and simulating creatures. These are in addition to
rlm@488:    jMonkeyEngine3's extensive library, which is documented elsewhere.
rlm@488: 
rlm@488: *** Simulation
rlm@488:    - =(world root-node key-map setup-fn update-fn)= :: create
rlm@488:         a simulation.
rlm@488:      - /root-node/     :: a =com.jme3.scene.Node= object which
rlm@488:           contains all of the objects that should be in the
rlm@488:           simulation.
rlm@488: 
rlm@488:      - /key-map/       :: a map from strings describing keys to
rlm@488:           functions that should be executed whenever that key is
rlm@488:           pressed. the functions should take a SimpleApplication
rlm@488:           object and a boolean value. The SimpleApplication is the
rlm@488:           current simulation that is running, and the boolean is true
rlm@488:           if the key is being pressed, and false if it is being
rlm@488:           released. As an example,
rlm@488:           #+BEGIN_SRC clojure
rlm@488:        {"key-j" (fn [game value] (if value (println "key j pressed")))}		  
rlm@488: 	  #+END_SRC
rlm@488: 	  is a valid key-map which will cause the simulation to print
rlm@488:           a message whenever the 'j' key on the keyboard is pressed.
rlm@488: 
rlm@488:      - /setup-fn/      :: a function that takes a =SimpleApplication=
rlm@488:           object. It is called once when initializing the simulation.
rlm@488:           Use it to create things like lights, change the gravity,
rlm@488:           initialize debug nodes, etc.
rlm@488: 
rlm@488:      - /update-fn/     :: this function takes a =SimpleApplication=
rlm@488:           object and a float and is called every frame of the
rlm@488:           simulation. The float tells how many seconds is has been
rlm@488:           since the last frame was rendered, according to whatever
rlm@488:           clock jme is currently using. The default is to use IsoTimer
rlm@488:           which will result in this value always being the same.
rlm@488: 
rlm@488:    - =(position-camera world position rotation)= :: set the position
rlm@488:         of the simulation's main camera.
rlm@488: 
rlm@488:    - =(enable-debug world)= :: turn on debug wireframes for each
rlm@488:         simulated object.
rlm@488: 
rlm@488:    - =(set-gravity world gravity)= :: set the gravity of a running
rlm@488:         simulation.
rlm@488: 
rlm@488:    - =(box length width height & {options})= :: create a box in the
rlm@488:         simulation. Options is a hash map specifying texture, mass,
rlm@488:         etc. Possible options are =:name=, =:color=, =:mass=,
rlm@488:         =:friction=, =:texture=, =:material=, =:position=,
rlm@488:         =:rotation=, =:shape=, and =:physical?=.
rlm@488: 
rlm@488:    - =(sphere radius & {options})= :: create a sphere in the simulation.
rlm@488:         Options are the same as in =box=.
rlm@488: 
rlm@488:    - =(load-blender-model file-name)= :: create a node structure
rlm@562:         representing the model described in a Blender file.
rlm@488: 
rlm@488:    - =(light-up-everything world)= :: distribute a standard compliment
rlm@517:         of lights throughout the simulation. Should be adequate for most
rlm@488:         purposes.
rlm@488: 
rlm@517:    - =(node-seq node)= :: return a recursive list of the node's
rlm@488:         children.
rlm@488: 
rlm@488:    - =(nodify name children)= :: construct a node given a node-name and
rlm@488:         desired children.
rlm@488: 
rlm@488:    - =(add-element world element)= :: add an object to a running world
rlm@488:         simulation.
rlm@488: 
rlm@488:    - =(set-accuracy world accuracy)= :: change the accuracy of the
rlm@488:         world's physics simulator.
rlm@488: 
rlm@488:    - =(asset-manager)= :: get an /AssetManager/, a jMonkeyEngine
rlm@488:         construct that is useful for loading textures and is required
rlm@488:         for smooth interaction with jMonkeyEngine library functions.
rlm@488: 
rlm@540:    - =(load-bullet)=   :: unpack native libraries and initialize the
rlm@540:         bullet physics subsystem. This function is required before
rlm@540:         other world building functions are called.
rlm@488: 	
rlm@488: *** Creature Manipulation / Import
rlm@488: 
rlm@488:    - =(body! creature)= :: give the creature a physical body.
rlm@488: 
rlm@488:    - =(vision! creature)= :: give the creature a sense of vision.
rlm@488:         Returns a list of functions which will each, when called
rlm@488:         during a simulation, return the vision data for the channel of
rlm@488:         one of the eyes. The functions are ordered depending on the
rlm@488:         alphabetical order of the names of the eye nodes in the
rlm@562:         Blender file. The data returned by the functions is a vector
rlm@488:         containing the eye's /topology/, a vector of coordinates, and
rlm@488:         the eye's /data/, a vector of RGB values filtered by the eye's
rlm@488:         sensitivity. 
rlm@488: 
rlm@488:    - =(hearing! creature)= :: give the creature a sense of hearing.
rlm@488:         Returns a list of functions, one for each ear, that when
rlm@488:         called will return a frame's worth of hearing data for that
rlm@488:         ear. The functions are ordered depending on the alphabetical
rlm@562:         order of the names of the ear nodes in the Blender file. The
rlm@540:         data returned by the functions is an array of PCM (pulse code
rlm@540:         modulated) wav data.
rlm@488: 
rlm@488:    - =(touch! creature)= :: give the creature a sense of touch. Returns
rlm@488:         a single function that must be called with the /root node/ of
rlm@488:         the world, and which will return a vector of /touch-data/
rlm@488:         one entry for each touch sensitive component, each entry of
rlm@488:         which contains a /topology/ that specifies the distribution of
rlm@488:         touch sensors, and the /data/, which is a vector of
rlm@488:         =[activation, length]= pairs for each touch hair.
rlm@488: 
rlm@488:    - =(proprioception! creature)= :: give the creature the sense of
rlm@488:         proprioception. Returns a list of functions, one for each
rlm@488:         joint, that when called during a running simulation will
rlm@517:         report the =[heading, pitch, roll]= of the joint.
rlm@488: 
rlm@488:    - =(movement! creature)= :: give the creature the power of movement.
rlm@488:         Creates a list of functions, one for each muscle, that when
rlm@488:         called with an integer, will set the recruitment of that
rlm@488:         muscle to that integer, and will report the current power
rlm@488:         being exerted by the muscle. Order of muscles is determined by
rlm@488:         the alphabetical sort order of the names of the muscle nodes.
rlm@488: 	
rlm@488: *** Visualization/Debug
rlm@488: 
rlm@488:    - =(view-vision)= :: create a function that when called with a list
rlm@488:         of visual data returned from the functions made by =vision!=, 
rlm@488:         will display that visual data on the screen.
rlm@488: 
rlm@488:    - =(view-hearing)= :: same as =view-vision= but for hearing.
rlm@488: 
rlm@488:    - =(view-touch)= :: same as =view-vision= but for touch.
rlm@488: 
rlm@488:    - =(view-proprioception)= :: same as =view-vision= but for
rlm@488:         proprioception.
rlm@488: 
rlm@540:    - =(view-movement)= :: same as =view-vision= but for muscles.
rlm@488: 
rlm@488:    - =(view anything)= :: =view= is a polymorphic function that allows
rlm@488:         you to inspect almost anything you could reasonably expect to
rlm@488:         be able to ``see'' in =CORTEX=.
rlm@488: 
rlm@488:    - =(text anything)= :: =text= is a polymorphic function that allows
rlm@488:         you to convert practically anything into a text string.	
rlm@488: 
rlm@488:    - =(println-repl anything)= :: print messages to clojure's repl
rlm@488:         instead of the simulation's terminal window.
rlm@488: 
rlm@488:    - =(mega-import-jme3)= :: for experimenting at the REPL. This
rlm@488:         function will import all jMonkeyEngine3 classes for immediate
rlm@488:         use.
rlm@488: 
rlm@517:    - =(display-dilated-time world timer)= :: Shows the time as it is
rlm@488:         flowing in the simulation on a HUD display.