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@453: * Empathy and Embodiment as problem solving strategies
rlm@437:   
rlm@437:   By the end of this thesis, you will have seen a novel approach to
rlm@437:   interpreting video using embodiment and empathy. You will have also
rlm@437:   seen one way to efficiently implement empathy for embodied
rlm@447:   creatures. Finally, you will become familiar with =CORTEX=, a system
rlm@447:   for designing and simulating creatures with rich senses, which you
rlm@447:   may choose to use in your own research.
rlm@437:   
rlm@441:   This is the core vision of my thesis: That one of the important ways
rlm@441:   in which we understand others is by imagining ourselves in their
rlm@441:   position and emphatically feeling experiences relative to our own
rlm@441:   bodies. By understanding events in terms of our own previous
rlm@441:   corporeal experience, we greatly constrain the possibilities of what
rlm@441:   would otherwise be an unwieldy exponential search. This extra
rlm@441:   constraint can be the difference between easily understanding what
rlm@441:   is happening in a video and being completely lost in a sea of
rlm@441:   incomprehensible color and movement.
rlm@435: 
rlm@436: ** Recognizing actions in video is extremely difficult
rlm@437: 
rlm@447:    Consider for example the problem of determining what is happening
rlm@447:    in a video of which this is one frame:
rlm@437: 
rlm@441:    #+caption: A cat drinking some water. Identifying this action is 
rlm@441:    #+caption: beyond the state of the art for computers.
rlm@441:    #+ATTR_LaTeX: :width 7cm
rlm@441:    [[./images/cat-drinking.jpg]]
rlm@441:    
rlm@441:    It is currently impossible for any computer program to reliably
rlm@447:    label such a video as ``drinking''. And rightly so -- it is a very
rlm@441:    hard problem! What features can you describe in terms of low level
rlm@441:    functions of pixels that can even begin to describe at a high level
rlm@441:    what is happening here?
rlm@437:   
rlm@447:    Or suppose that you are building a program that recognizes chairs.
rlm@448:    How could you ``see'' the chair in figure \ref{hidden-chair}?
rlm@441:    
rlm@441:    #+caption: The chair in this image is quite obvious to humans, but I 
rlm@448:    #+caption: doubt that any modern computer vision program can find it.
rlm@441:    #+name: hidden-chair
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@441:    [[./images/fat-person-sitting-at-desk.jpg]]
rlm@441:    
rlm@441:    Finally, how is it that you can easily tell the difference between
rlm@441:    how the girls /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@441:    #+caption: are activated between the two images.
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@441:    on in our minds as we easily solve these recognition problems.
rlm@441:    
rlm@441:    The hidden chairs show us that we are strongly triggered by cues
rlm@447:    relating to the position of human bodies, and that we can determine
rlm@447:    the overall physical configuration of a human body even if much of
rlm@447:    that body is occluded.
rlm@437: 
rlm@441:    The picture of the girl pushing against the wall tells us that we
rlm@441:    have common sense knowledge about the kinetics of our own bodies.
rlm@441:    We know well how our muscles would have to work to maintain us in
rlm@441:    most positions, and we can easily project this self-knowledge to
rlm@441:    imagined positions triggered by images of the human body.
rlm@441: 
rlm@441: ** =EMPATH= neatly solves recognition problems  
rlm@441:    
rlm@441:    I propose a system that can express the types of recognition
rlm@441:    problems above in a form amenable to computation. It is split into
rlm@441:    four parts:
rlm@441: 
rlm@448:    - Free/Guided Play :: The creature moves around and experiences the
rlm@448:         world through its unique perspective. Many otherwise
rlm@448:         complicated actions are easily described in the language of a
rlm@448:         full suite of body-centered, rich senses. For example,
rlm@448:         drinking is the feeling of water sliding 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@448:    - Posture Imitation :: When trying to interpret a video or image,
rlm@448:         the creature takes a model of itself and aligns it with
rlm@448:         whatever it sees. This alignment can 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@448:    - 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@448:         extent trigger previous experience. Segments of previous
rlm@448:         experiences are stitched together to form a coherent and
rlm@448:         complete sensory portrait of the scene.
rlm@448:    - Recognition      :: With the scene described in terms of first
rlm@448:         person sensory events, the creature can now run its
rlm@447:         action-identification programs on this synthesized sensory
rlm@447:         data, just as it would if it were actually experiencing the
rlm@447:         scene 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@447:    
rlm@441:    For example, I think humans are able to label the cat video as
rlm@447:    ``drinking'' because they imagine /themselves/ as the cat, and
rlm@441:    imagine putting their face up against a stream of water and
rlm@441:    sticking out their tongue. In that imagined world, they can feel
rlm@441:    the cool water hitting their tongue, and feel the water entering
rlm@447:    their body, and are able to recognize that /feeling/ as drinking.
rlm@447:    So, the label of the action is not really in the pixels of the
rlm@447:    image, but is found clearly in a simulation inspired by those
rlm@447:    pixels. An imaginative system, having been trained on drinking and
rlm@447:    non-drinking examples and learning that the most important
rlm@447:    component of drinking is the feeling of water sliding down one's
rlm@447:    throat, would analyze a video of a cat drinking in the following
rlm@447:    manner:
rlm@441:    
rlm@447:    1. Create a physical model of the video by putting a ``fuzzy''
rlm@447:       model of its own body in place of the cat. Possibly also create
rlm@447:       a simulation of the stream of water.
rlm@441: 
rlm@441:    2. Play out this simulated scene and generate imagined sensory
rlm@441:       experience. This will include relevant muscle contractions, a
rlm@441:       close up view of the stream from the cat's perspective, and most
rlm@441:       importantly, the imagined feeling of water entering the
rlm@443:       mouth. The imagined sensory experience can come from a
rlm@441:       simulation of the event, but can also be pattern-matched from
rlm@441:       previous, similar embodied experience.
rlm@441: 
rlm@441:    3. The action is now easily identified as drinking by the sense of
rlm@441:       taste alone. The other senses (such as the tongue moving in and
rlm@441:       out) help to give plausibility to the simulated action. Note that
rlm@441:       the sense of vision, while critical in creating the simulation,
rlm@441:       is not critical for identifying the action from the simulation.
rlm@441: 
rlm@441:    For the chair examples, the process is even easier:
rlm@441: 
rlm@441:     1. Align a model of your body to the person in the image.
rlm@441: 
rlm@441:     2. Generate proprioceptive sensory data from this alignment.
rlm@437:   
rlm@441:     3. Use the imagined proprioceptive data as a key to lookup related
rlm@441:        sensory experience associated with that particular proproceptive
rlm@441:        feeling.
rlm@437: 
rlm@443:     4. Retrieve the feeling of your bottom resting on a surface, your
rlm@443:        knees bent, and your leg muscles relaxed.
rlm@437: 
rlm@441:     5. This sensory information is consistent with the =sitting?=
rlm@441:        sensory predicate, so you (and the entity in the image) must be
rlm@441:        sitting.
rlm@440: 
rlm@441:     6. There must be a chair-like object since you are sitting.
rlm@440: 
rlm@441:    Empathy offers yet another alternative to the age-old AI
rlm@441:    representation question: ``What is a chair?'' --- A chair is the
rlm@441:    feeling of sitting.
rlm@441: 
rlm@441:    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@447:    #+caption: =EMPATH= recognized and classified each of these poses by
rlm@447:    #+caption: inferring the complete sensory experience from 
rlm@447:    #+caption: proprioceptive data.
rlm@441:    #+name: worm-recognition-intro
rlm@446:    #+ATTR_LaTeX: :width 15cm
rlm@445:    [[./images/worm-poses.png]]
rlm@441:    
rlm@441:    One powerful advantage of empathic problem solving is that it
rlm@441:    factors the action recognition problem into two easier problems. To
rlm@441:    use empathy, you need an /aligner/, which takes the video and a
rlm@441:    model of your body, and aligns the model with the video. Then, you
rlm@441:    need a /recognizer/, which uses the aligned model to interpret the
rlm@441:    action. The power in this method lies in the fact that you describe
rlm@448:    all actions form a body-centered viewpoint. You are less tied to
rlm@447:    the particulars of any visual representation of the actions. If you
rlm@441:    teach the system what ``running'' is, and you have a good enough
rlm@441:    aligner, the system will from then on be able to recognize running
rlm@441:    from any point of view, even strange points of view like above or
rlm@441:    underneath the runner. This is in contrast to action recognition
rlm@448:    schemes that try to identify actions using a non-embodied approach.
rlm@448:    If these systems learn about running as viewed from the side, they
rlm@448:    will not automatically be able to recognize running from any other
rlm@448:    viewpoint.
rlm@441: 
rlm@441:    Another powerful advantage is that using the language of multiple
rlm@441:    body-centered rich senses to describe body-centerd actions offers a
rlm@441:    massive boost in descriptive capability. Consider how difficult it
rlm@441:    would be to compose a set of HOG filters to describe the action of
rlm@447:    a simple worm-creature ``curling'' so that its head touches its
rlm@447:    tail, and then behold the simplicity of describing thus action in a
rlm@441:    language designed for the task (listing \ref{grand-circle-intro}):
rlm@441: 
rlm@446:    #+caption: Body-centerd actions are best expressed in a body-centered 
rlm@446:    #+caption: language. This code detects when the worm has curled into a 
rlm@446:    #+caption: full circle. Imagine how you would replicate this functionality
rlm@446:    #+caption: using low-level pixel features such as HOG filters!
rlm@446:    #+name: grand-circle-intro
rlm@452:    #+attr_latex: [htpb]
rlm@452: #+begin_listing clojure
rlm@446:    #+begin_src clojure
rlm@446: (defn grand-circle?
rlm@446:   "Does the worm form a majestic circle (one end touching the other)?"
rlm@446:   [experiences]
rlm@446:   (and (curled? experiences)
rlm@446:        (let [worm-touch (:touch (peek experiences))
rlm@446:              tail-touch (worm-touch 0)
rlm@446:              head-touch (worm-touch 4)]
rlm@446:          (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
rlm@446:               (< 0.55 (contact worm-segment-top-tip    head-touch))))))
rlm@446:    #+end_src
rlm@446:    #+end_listing
rlm@446: 
rlm@435: 
rlm@449: **  =CORTEX= is a toolkit for building sensate creatures
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@448:    more projects than just this one. =CORTEX= was necessary to meet a
rlm@448:    need among AI researchers at CSAIL and beyond, which is that people
rlm@448:    often will invent neat ideas that are best expressed in the
rlm@448:    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@448:    would be simple to execute (such as =EMPATH=), but attached to them
rlm@448:    is the multi-month effort to make a good creature simulator. Often,
rlm@448:    that initial investment of time proves to be too much, and the
rlm@448:    project must make do with a lesser environment.
rlm@435: 
rlm@448:    =CORTEX= is well suited as an environment for embodied AI research
rlm@448:    for three reasons:
rlm@448: 
rlm@448:    - You can create new creatures using Blender, a popular 3D modeling
rlm@448:      program. Each sense can be specified using special blender nodes
rlm@448:      with biologically inspired paramaters. You need not write any
rlm@448:      code to create a creature, and can use a wide library of
rlm@448:      pre-existing blender models as a base for your own creatures.
rlm@448: 
rlm@448:    - =CORTEX= implements a wide variety of senses, including touch,
rlm@448:      proprioception, vision, hearing, and muscle tension. Complicated
rlm@448:      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@448:      png image files. In particular, =CORTEX= implements more
rlm@448:      comprehensive hearing than any other creature simulation system
rlm@448:      available. 
rlm@448: 
rlm@448:    - =CORTEX= supports any number of creatures and any number of
rlm@448:      senses. Time in =CORTEX= dialates so that the simulated creatures
rlm@448:      always precieve a perfectly smooth flow of time, regardless of
rlm@448:      the actual computational load.
rlm@448: 
rlm@448:    =CORTEX= is built on top of =jMonkeyEngine3=, which is a video game
rlm@448:    engine designed to create cross-platform 3D desktop games. =CORTEX=
rlm@448:    is mainly written in clojure, a dialect of =LISP= that runs on the
rlm@448:    java virtual machine (JVM). The API for creating and simulating
rlm@449:    creatures and senses is entirely expressed in clojure, though many
rlm@449:    senses are implemented at the layer of jMonkeyEngine or below. For
rlm@449:    example, for the sense of hearing I use a layer of clojure code on
rlm@449:    top of a layer of java JNI bindings that drive a layer of =C++=
rlm@449:    code which implements a modified version of =OpenAL= to support
rlm@449:    multiple listeners. =CORTEX= is the only simulation environment
rlm@449:    that I know of that can support multiple entities that can each
rlm@449:    hear the world from their own perspective. Other senses also
rlm@449:    require a small layer of Java code. =CORTEX= also uses =bullet=, a
rlm@449:    physics simulator written in =C=.
rlm@448: 
rlm@448:    #+caption: Here is the worm from above modeled in Blender, a free 
rlm@448:    #+caption: 3D-modeling program. Senses and joints are described
rlm@448:    #+caption: using special nodes in Blender.
rlm@448:    #+name: worm-recognition-intro
rlm@448:    #+ATTR_LaTeX: :width 12cm
rlm@448:    [[./images/blender-worm.png]]
rlm@448: 
rlm@449:    Here are some thing 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@449:    - exploration of exoitic senses and effectors that are not possible
rlm@449:      in the real world (such as telekenisis 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@448:    their own independent senses and ran them all at only 1/80 real
rlm@448:    time. 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@449:    \includegraphics[width=9.5in]{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@451:    displayed on the right, 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@437: ** Contributions
rlm@435: 
rlm@451:    - I built =CORTEX=, a comprehensive platform for embodied AI
rlm@451:      experiments. =CORTEX= supports many features lacking in other
rlm@451:      systems, such proper simulation of hearing. It is easy to create
rlm@451:      new =CORTEX= creatures using Blender, a free 3D modeling program.
rlm@449: 
rlm@451:    - I built =EMPATH=, which uses =CORTEX= to identify the actions of
rlm@451:      a worm-like creature using a computational model of empathy.
rlm@449:    
rlm@436: * Building =CORTEX=
rlm@435: 
rlm@436: ** To explore embodiment, we need a world, body, and senses
rlm@435: 
rlm@436: ** Because of Time, simulation is perferable to reality
rlm@435: 
rlm@436: ** Video game engines are a great starting point
rlm@435: 
rlm@436: ** Bodies are composed of segments connected by joints
rlm@435: 
rlm@436: ** Eyes reuse standard video game components
rlm@436: 
rlm@436: ** Hearing is hard; =CORTEX= does it right
rlm@436: 
rlm@436: ** Touch uses hundreds of hair-like elements
rlm@436: 
rlm@440: ** Proprioception is the sense that makes everything ``real''
rlm@436: 
rlm@436: ** Muscles are both effectors and sensors
rlm@436: 
rlm@436: ** =CORTEX= brings complex creatures to life!
rlm@436: 
rlm@436: ** =CORTEX= enables many possiblities for further research
rlm@435: 
rlm@435: * Empathy 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@449:   it full compliment of imagined sensory data. I can then use the
rlm@449:   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@449:   #+caption: Here is the worm with which we will be working. 
rlm@449:   #+caption: It is composed of 5 segments. Each segment has a 
rlm@449:   #+caption: pair of extensor and flexor muscles. Each of the 
rlm@449:   #+caption: worm's four joints is a hinge joint which allows 
rlm@451:   #+caption: about 30 degrees of rotation to either side. Each segment
rlm@449:   #+caption: of the worm is touch-capable and has a uniform 
rlm@449:   #+caption: distribution of touch sensors on each of its faces.
rlm@449:   #+caption: Each joint has a proprioceptive sense to detect 
rlm@449:   #+caption: relative positions. The worm segments are all the 
rlm@449:   #+caption: same except for the first one, which has a much
rlm@449:   #+caption: higher weight than the others to allow for easy 
rlm@449:   #+caption: 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@449:   #+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@449:   #+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@436: ** Embodiment factors action recognition into managable 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@449:    of senses, and an imaganitive 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@449:    recognize actions: The imaganitive 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@449:    Embodied representations using multiple senses such as touch,
rlm@449:    proprioception, and muscle tension turns out be be exceedingly
rlm@449:    efficient at describing body-centered actions. It is the ``right
rlm@449:    language for the job''. For example, it takes only around 5 lines
rlm@449:    of LISP 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@449:    relies on a fourier analysis of muscle contraction, and
rlm@449:    =grand-circle?= relies on touch and reuses =curled?= as a gaurd.
rlm@449:    
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@449:    #+caption: independent and ignores vermopomorphic differences such as 
rlm@449:    #+caption: worm textures and colors.
rlm@449:    #+name: curled
rlm@452:    #+attr_latex: [htpb]
rlm@452: #+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@449: 
rlm@449:    #+caption: Program for summarizing the touch information in a patch 
rlm@449:    #+caption: of skin.
rlm@449:    #+name: touch-summary
rlm@452:    #+attr_latex: [htpb]
rlm@452: 
rlm@452: #+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@449:    #+caption: proprioction at all.
rlm@449:    #+name: resting
rlm@452:    #+attr_latex: [htpb]
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:    #+attr_latex: [htpb]
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@449:    #+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@449:    #+caption: signigicant 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:    #+attr_latex: [htpb]
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:    #+attr_latex: [htpb]
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@449:    #+caption: work together to classify the behaviour 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@451:    confused in visual details of the worm. Each one is frame
rlm@451:    independent, but more than that, they are each indepent of
rlm@449:    irrelevant visual details of the worm and the environment. They
rlm@449:    will work regardless of whether the worm is a different color or
rlm@451:    hevaily textured, or if the environment has strange lighting.
rlm@449: 
rlm@449:    The trick now is to make the action predicates work even when the
rlm@449:    sensory data on which they depend is absent. If I can do that, then
rlm@449:    I will have gained much,
rlm@435: 
rlm@436: ** \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@451:    the sensations the worm can practicaly 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@451:    from physical embodiment. The proprioceptive components are
rlm@451:    completely smooth, because in order for the worm to move from one
rlm@451:    position to another, it must pass through the intermediate
rlm@451:    positions. The path invariably forms loops as actions are repeated.
rlm@451:    Finally and most importantly, proprioception actually gives very
rlm@451:    strong inference about the other senses. For example, when the worm
rlm@451:    is flat, 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@451:    worm would be moving and would not be perfectly flat. In order to
rlm@451:    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@451:    as then. If you observe multiple frames of proprioceptive data,
rlm@451:    then you can become increasingly confident about the exact
rlm@451:    activations of the worm's muscles, because it generally takes a
rlm@451:    unique combination of muscle contractions to transform the worm's
rlm@451:    body along a specific path through \Phi-space.
rlm@449: 
rlm@449:    There is a simple way of taking \Phi-space and the total ordering
rlm@449:    provided by an experience vector and reliably infering the rest of
rlm@449:    the senses.
rlm@435: 
rlm@436: ** Empathy is the process of tracing though \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@451:    First, group the experiences into tiered proprioceptive bins. I use
rlm@451:    powers of 10 and 3 bins, and the smallest bin has an approximate
rlm@451:    size of 0.001 radians in all proprioceptive dimensions.
rlm@450:    
rlm@450:    Then, given a sequence of proprioceptive input, generate a set of
rlm@451:    matching experience records for each input, using the tiered
rlm@451:    proprioceptive bins. 
rlm@449: 
rlm@450:    Finally, to infer sensory data, select the longest consective chain
rlm@451:    of experiences. Conecutive experience means that the experiences
rlm@451:    appear next to each other in the experience vector.
rlm@449: 
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@450:       an instant, this flattness will not be interpreted as implying
rlm@450:       that the worm has its muscles relaxed, since the flattness 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:    #+attr_latex: [htpb]
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@450:    the propriceptive 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@451:    #+caption: experiences to explain proprioceptive worm data.
rlm@451:    #+name: phi-space-history-scan
rlm@451:    #+ATTR_LaTeX: :width 10cm
rlm@451:    [[./images/aurellem-gray.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@451:    excercises. If during recognition the worm rests on the ground for
rlm@451:    five seconds, =longest-thread= will accomodate this five second
rlm@451:    rest period by looping the one second rest chain five times.
rlm@451: 
rlm@451:    =longest-thread= takes time proportinal to the average number of
rlm@451:    entries in a proprioceptive bin, because for each element in the
rlm@451:    starting bin it performes a series of set lookups in the preceeding
rlm@451:    bins. If the total history is limited, then this is only a constant
rlm@451:    multiple times the number of entries in the starting bin. This
rlm@451:    analysis also applies even if the action requires multiple longest
rlm@451:    chains -- it's still the average number of entries in a
rlm@451:    proprioceptive bin times the desired chain length. Because
rlm@451:    =longest-thread= is so efficient and simple, I can interpret
rlm@451:    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:    #+attr_latex: [htpb]
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@451:    Therefore, I simply replicate the most recent sensory experience to
rlm@451:    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:    #+attr_latex: [htpb]
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@441: ** Efficient action recognition with =EMPATH=
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@451:    exercices and gatheres 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@451:    #+caption: of ``exercices'' that include all the action predicates.
rlm@451:    #+name: generate-phi-space
rlm@452:    #+attr_latex: [htpb]
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@451:    #+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:    #+attr_latex: [htpb]
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@451:            :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@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@451:   #+caption: (The last panel shows a composite image of \emph{wriggling}, 
rlm@451:   #+caption: a dynamic pose.)
rlm@451:   #+name: empathy-debug-image
rlm@451:   #+ATTR_LaTeX: :width 10cm :placement [H]
rlm@451:   [[./images/empathy-1.png]]
rlm@451: 
rlm@451:   One way to measure the performance of =EMPATH= is to compare the
rlm@451:   sutiability 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:    #+attr_latex: [htpb]
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@451:   program defined in listing \ref{generate-phi-space}, and then doing
rlm@451:   a similar pattern of activity manually yeilds an accuracy of around
rlm@451:   73%. This is based on very limited worm experience. By training the
rlm@451:   worm for longer, the accuracy dramatically improves.
rlm@451: 
rlm@451:    #+caption: Program to generate \Phi-space using manual training.
rlm@451:    #+name: manual-phi-space
rlm@452:    #+attr_latex: [htpb]
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@451:   After about 1 minute of manual training, I was able to achieve 95%
rlm@451:   accuracy on manual testing of the worm using =init-interactive= and
rlm@452:   =test-empathy-accuracy=. The majority of errors are near the
rlm@452:   boundaries of transitioning from one type of action to another.
rlm@452:   During these transitions the exact label for the action is more open
rlm@452:   to interpretation, and dissaggrement between empathy and experience
rlm@452:   is more excusable.
rlm@450: 
rlm@449: ** Digression: bootstrapping touch using free exploration
rlm@449: 
rlm@452:    In the previous section I showed how to compute actions in terms of
rlm@452:    body-centered predicates which relied averate touch activation of
rlm@452:    pre-defined regions of the worm's skin. What if, instead of recieving
rlm@452:    touch pre-grouped into the six faces of each worm segment, the true
rlm@452:    topology of the worm's skin was unknown? This is more similiar to how
rlm@452:    a nerve fiber bundle might be arranged. While two fibers that are
rlm@452:    close in a nerve bundle /might/ correspond to two touch sensors that
rlm@452:    are close together on the skin, the process of taking a complicated
rlm@452:    surface and forcing it into essentially a circle requires some cuts
rlm@452:    and rerragenments.
rlm@452:    
rlm@452:    In this section I show how to automatically learn the skin-topology of
rlm@452:    a worm segment by free exploration. As the worm rolls around on the
rlm@452:    floor, large sections of its surface get activated. If the worm has
rlm@452:    stopped moving, then whatever region of skin that is touching the
rlm@452:    floor is probably an important region, and should be recorded.
rlm@452:    
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@452:    similiar touch regions. While for some purposes the subtle
rlm@452:    differences between these regions will be important, for my
rlm@452:    purposes I colapse them into mostly non-overlapping sets using
rlm@452:    =remove-similiar= in listing \ref{remove-similiar}
rlm@452: 
rlm@452:    #+caption: Program to take a lits of set of points and ``collapse them''
rlm@452:    #+caption: so that the remaining sets in the list are siginificantly 
rlm@452:    #+caption: different from each other. Prefer smaller sets to larger ones.
rlm@452:    #+name: remove-similiar
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@452:    #+caption: sensations by stable ones, collapse similiar 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@452:    The only thing remining 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: 
rlm@452:    #+caption: After completing its adventures, the worm now knows 
rlm@452:    #+caption: how its touch sensors are arranged along its skin. These 
rlm@452:    #+caption: are the regions that were deemed important by 
rlm@452:    #+caption: =learn-touch-regions=. Note that the worm has discovered
rlm@452:    #+caption: that it has six sides.
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@452:    completely scrambled. The cross shape is just for convienence. This
rlm@452:    example justifies the use of pre-defined touch regions in =EMPATH=.
rlm@452: 
rlm@432: * Contributions
rlm@454:   
rlm@461:   In this thesis you have seen the =CORTEX= system, a complete
rlm@461:   environment for creating simulated creatures. You have seen how to
rlm@461:   implement five senses including touch, proprioception, hearing,
rlm@461:   vision, and muscle tension. You have seen how to create new creatues
rlm@461:   using blender, a 3D modeling tool. I hope that =CORTEX= will be
rlm@461:   useful in further research projects. To this end I have included the
rlm@461:   full source to =CORTEX= along with a large suite of tests and
rlm@461:   examples. I have also created a user guide for =CORTEX= which is
rlm@461:   inculded in an appendix to this thesis.
rlm@447: 
rlm@461:   You have also seen how I used =CORTEX= as a platform to attach the
rlm@461:   /action recognition/ problem, which is the problem of recognizing
rlm@461:   actions in video. You saw a simple system called =EMPATH= which
rlm@461:   ientifies actions by first describing actions in a body-centerd,
rlm@461:   rich sense language, then infering a full range of sensory
rlm@461:   experience from limited data using previous experience gained from
rlm@461:   free play.
rlm@447: 
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@461:   the ground. 
rlm@461: 
rlm@461:   In conclusion, the main contributions of this thesis are:
rlm@461: 
rlm@461:   - =CORTEX=, a system for creating simulated creatures with rich
rlm@461:     senses.
rlm@461:   - =EMPATH=, a program for recognizing actions by imagining sensory
rlm@461:     experience. 
rlm@447: 
rlm@447: # An anatomical joke:
rlm@447: # - Training
rlm@447: # - Skeletal imitation
rlm@447: # - Sensory fleshing-out
rlm@447: # - Classification