cortex: thesis/cortex.org annotate

annotate thesis/cortex.org @ 547:5d89879fc894

couple hours worth of edits.

author	Robert McIntyre <rlm@mit.edu>
date	Mon, 28 Apr 2014 15:10:59 -0400
parents	b2c66ea58c39
children	0b891e0dd809

rev	line source
rlm@425	1 #+title: =CORTEX=
rlm@425	2 #+author: Robert McIntyre
rlm@425	3 #+email: rlm@mit.edu
rlm@425	4 #+description: Using embodied AI to facilitate Artificial Imagination.
rlm@425	5 #+keywords: AI, clojure, embodiment
rlm@451	6 #+LaTeX_CLASS_OPTIONS: [nofloat]
rlm@422	7
rlm@465	8 * COMMENT templates
rlm@470	9 #+caption:
rlm@470	10 #+caption:
rlm@470	11 #+caption:
rlm@470	12 #+caption:
rlm@470	13 #+name: name
rlm@470	14 #+begin_listing clojure
rlm@479	15 #+BEGIN_SRC clojure
rlm@479	16 #+END_SRC
rlm@470	17 #+end_listing
rlm@465	18
rlm@470	19 #+caption:
rlm@470	20 #+caption:
rlm@470	21 #+caption:
rlm@470	22 #+name: name
rlm@470	23 #+ATTR_LaTeX: :width 10cm
rlm@470	24 [[./images/aurellem-gray.png]]
rlm@470	25
rlm@470	26 #+caption:
rlm@470	27 #+caption:
rlm@470	28 #+caption:
rlm@470	29 #+caption:
rlm@470	30 #+name: name
rlm@470	31 #+begin_listing clojure
rlm@475	32 #+BEGIN_SRC clojure
rlm@475	33 #+END_SRC
rlm@470	34 #+end_listing
rlm@470	35
rlm@470	36 #+caption:
rlm@470	37 #+caption:
rlm@470	38 #+caption:
rlm@470	39 #+name: name
rlm@470	40 #+ATTR_LaTeX: :width 10cm
rlm@470	41 [[./images/aurellem-gray.png]]
rlm@470	42
rlm@465	43
rlm@541	44 * Empathy \& Embodiment: problem solving strategies
rlm@516	45
rlm@547	46 By the end of this thesis, you will have a novel approach to
rlm@547	47 representing an recognizing physical actions using embodiment and
rlm@547	48 empathy. You will also see one way to efficiently implement physical
rlm@547	49 empathy for embodied creatures. Finally, you will become familiar
rlm@547	50 with =CORTEX=, a system for designing and simulating creatures with
rlm@547	51 rich senses, which I have designed as a library that you can use in
rlm@547	52 your own research. Note that I /do not/ process video directly --- I
rlm@547	53 start with knowledge of the positions of a creature's body parts and
rlm@547	54 works from there.
rlm@437	55
rlm@516	56 This is the core vision of my thesis: That one of the important ways
rlm@516	57 in which we understand others is by imagining ourselves in their
rlm@516	58 position and emphatically feeling experiences relative to our own
rlm@516	59 bodies. By understanding events in terms of our own previous
rlm@516	60 corporeal experience, we greatly constrain the possibilities of what
rlm@516	61 would otherwise be an unwieldy exponential search. This extra
rlm@516	62 constraint can be the difference between easily understanding what
rlm@516	63 is happening in a video and being completely lost in a sea of
rlm@516	64 incomprehensible color and movement.
rlm@516	65
rlm@525	66 ** The problem: recognizing actions is hard!
rlm@511	67
rlm@516	68 Examine the following image. What is happening? As you, and indeed
rlm@516	69 very young children, can easily determine, this is an image of
rlm@516	70 drinking.
rlm@516	71
rlm@441	72 #+caption: A cat drinking some water. Identifying this action is
rlm@511	73 #+caption: beyond the capabilities of existing computer vision systems.
rlm@441	74 #+ATTR_LaTeX: :width 7cm
rlm@441	75 [[./images/cat-drinking.jpg]]
rlm@511	76
rlm@511	77 Nevertheless, it is beyond the state of the art for a computer
rlm@516	78 vision program to describe what's happening in this image. Part of
rlm@516	79 the problem is that many computer vision systems focus on
rlm@516	80 pixel-level details or comparisons to example images (such as
rlm@516	81 \cite{volume-action-recognition}), but the 3D world is so variable
rlm@517	82 that it is hard to describe the world in terms of possible images.
rlm@511	83
rlm@547	84 In fact, the contents of a scene may have much less to do with
rlm@547	85 pixel probabilities than with recognizing various affordances:
rlm@547	86 things you can move, objects you can grasp, spaces that can be
rlm@547	87 filled . For example, what processes might enable you to see the
rlm@547	88 chair in figure \ref{hidden-chair}?
rlm@516	89
rlm@525	90 #+caption: The chair in this image is quite obvious to humans, but
rlm@525	91 #+caption: it can't be found by any modern computer vision program.
rlm@441	92 #+name: hidden-chair
rlm@441	93 #+ATTR_LaTeX: :width 10cm
rlm@441	94 [[./images/fat-person-sitting-at-desk.jpg]]
rlm@511	95
rlm@441	96 Finally, how is it that you can easily tell the difference between
rlm@441	97 how the girls /muscles/ are working in figure \ref{girl}?
rlm@441	98
rlm@441	99 #+caption: The mysterious ``common sense'' appears here as you are able
rlm@441	100 #+caption: to discern the difference in how the girl's arm muscles
rlm@441	101 #+caption: are activated between the two images.
rlm@441	102 #+name: girl
rlm@448	103 #+ATTR_LaTeX: :width 7cm
rlm@441	104 [[./images/wall-push.png]]
rlm@437	105
rlm@441	106 Each of these examples tells us something about what might be going
rlm@517	107 on in our minds as we easily solve these recognition problems:
rlm@441	108
rlm@547	109 - The hidden chair shows us that we are strongly triggered by cues
rlm@547	110 relating to the position of human bodies, and that we can
rlm@547	111 determine the overall physical configuration of a human body even
rlm@547	112 if much of that body is occluded.
rlm@547	113
rlm@547	114 - The picture of the girl pushing against the wall tells us that we
rlm@547	115 have common sense knowledge about the kinetics of our own bodies.
rlm@547	116 We know well how our muscles would have to work to maintain us in
rlm@547	117 most positions, and we can easily project this self-knowledge to
rlm@547	118 imagined positions triggered by images of the human body.
rlm@547	119
rlm@547	120 - The cat tells us that imagination of some kind plays an important
rlm@547	121 role in understanding actions. The question is: Can we be more
rlm@547	122 precise about what sort of imagination is required to understand
rlm@547	123 these actions?
rlm@517	124
rlm@511	125 ** A step forward: the sensorimotor-centered approach
rlm@516	126
rlm@511	127 In this thesis, I explore the idea that our knowledge of our own
rlm@516	128 bodies, combined with our own rich senses, enables us to recognize
rlm@516	129 the actions of others.
rlm@516	130
rlm@516	131 For example, I think humans are able to label the cat video as
rlm@516	132 ``drinking'' because they imagine /themselves/ as the cat, and
rlm@516	133 imagine putting their face up against a stream of water and
rlm@516	134 sticking out their tongue. In that imagined world, they can feel
rlm@516	135 the cool water hitting their tongue, and feel the water entering
rlm@516	136 their body, and are able to recognize that /feeling/ as drinking.
rlm@516	137 So, the label of the action is not really in the pixels of the
rlm@547	138 image, but is found clearly in a simulation / recollection inspired
rlm@547	139 by those pixels. An imaginative system, having been trained on
rlm@547	140 drinking and non-drinking examples and learning that the most
rlm@547	141 important component of drinking is the feeling of water sliding
rlm@547	142 down one's throat, would analyze a video of a cat drinking in the
rlm@547	143 following manner:
rlm@516	144
rlm@516	145 1. Create a physical model of the video by putting a ``fuzzy''
rlm@516	146 model of its own body in place of the cat. Possibly also create
rlm@516	147 a simulation of the stream of water.
rlm@516	148
rlm@517	149 2. ``Play out'' this simulated scene and generate imagined sensory
rlm@516	150 experience. This will include relevant muscle contractions, a
rlm@516	151 close up view of the stream from the cat's perspective, and most
rlm@517	152 importantly, the imagined feeling of water entering the mouth.
rlm@517	153 The imagined sensory experience can come from a simulation of
rlm@517	154 the event, but can also be pattern-matched from previous,
rlm@517	155 similar embodied experience.
rlm@516	156
rlm@516	157 3. The action is now easily identified as drinking by the sense of
rlm@516	158 taste alone. The other senses (such as the tongue moving in and
rlm@516	159 out) help to give plausibility to the simulated action. Note that
rlm@516	160 the sense of vision, while critical in creating the simulation,
rlm@516	161 is not critical for identifying the action from the simulation.
rlm@516	162
rlm@516	163 For the chair examples, the process is even easier:
rlm@516	164
rlm@516	165 1. Align a model of your body to the person in the image.
rlm@516	166
rlm@516	167 2. Generate proprioceptive sensory data from this alignment.
rlm@516	168
rlm@516	169 3. Use the imagined proprioceptive data as a key to lookup related
rlm@517	170 sensory experience associated with that particular proprioceptive
rlm@516	171 feeling.
rlm@516	172
rlm@516	173 4. Retrieve the feeling of your bottom resting on a surface, your
rlm@516	174 knees bent, and your leg muscles relaxed.
rlm@516	175
rlm@516	176 5. This sensory information is consistent with your =sitting?=
rlm@516	177 sensory predicate, so you (and the entity in the image) must be
rlm@516	178 sitting.
rlm@516	179
rlm@516	180 6. There must be a chair-like object since you are sitting.
rlm@516	181
rlm@516	182 Empathy offers yet another alternative to the age-old AI
rlm@516	183 representation question: ``What is a chair?'' --- A chair is the
rlm@516	184 feeling of sitting!
rlm@516	185
rlm@516	186 One powerful advantage of empathic problem solving is that it
rlm@516	187 factors the action recognition problem into two easier problems. To
rlm@516	188 use empathy, you need an /aligner/, which takes the video and a
rlm@516	189 model of your body, and aligns the model with the video. Then, you
rlm@516	190 need a /recognizer/, which uses the aligned model to interpret the
rlm@516	191 action. The power in this method lies in the fact that you describe
rlm@521	192 all actions from a body-centered viewpoint. You are less tied to
rlm@516	193 the particulars of any visual representation of the actions. If you
rlm@516	194 teach the system what ``running'' is, and you have a good enough
rlm@516	195 aligner, the system will from then on be able to recognize running
rlm@547	196 from any point of view -- even strange points of view like above or
rlm@516	197 underneath the runner. This is in contrast to action recognition
rlm@516	198 schemes that try to identify actions using a non-embodied approach.
rlm@516	199 If these systems learn about running as viewed from the side, they
rlm@516	200 will not automatically be able to recognize running from any other
rlm@516	201 viewpoint.
rlm@516	202
rlm@516	203 Another powerful advantage is that using the language of multiple
rlm@547	204 body-centered rich senses to describe body-centered actions offers
rlm@547	205 a massive boost in descriptive capability. Consider how difficult
rlm@547	206 it would be to compose a set of HOG (Histogram of Oriented
rlm@547	207 Gradients) filters to describe the action of a simple worm-creature
rlm@547	208 ``curling'' so that its head touches its tail, and then behold the
rlm@547	209 simplicity of describing thus action in a language designed for the
rlm@547	210 task (listing \ref{grand-circle-intro}):
rlm@516	211
rlm@517	212 #+caption: Body-centered actions are best expressed in a body-centered
rlm@516	213 #+caption: language. This code detects when the worm has curled into a
rlm@516	214 #+caption: full circle. Imagine how you would replicate this functionality
rlm@516	215 #+caption: using low-level pixel features such as HOG filters!
rlm@516	216 #+name: grand-circle-intro
rlm@516	217 #+begin_listing clojure
rlm@516	218 #+begin_src clojure
rlm@516	219 (defn grand-circle?
rlm@516	220 "Does the worm form a majestic circle (one end touching the other)?"
rlm@516	221 [experiences]
rlm@516	222 (and (curled? experiences)
rlm@516	223 (let [worm-touch (:touch (peek experiences))
rlm@516	224 tail-touch (worm-touch 0)
rlm@516	225 head-touch (worm-touch 4)]
rlm@516	226 (and (< 0.2 (contact worm-segment-bottom-tip tail-touch))
rlm@516	227 (< 0.2 (contact worm-segment-top-tip head-touch))))))
rlm@516	228 #+end_src
rlm@516	229 #+end_listing
rlm@516	230
rlm@517	231 ** =EMPATH= recognizes actions using empathy
rlm@517	232
rlm@517	233 Exploring these ideas further demands a concrete implementation, so
rlm@517	234 first, I built a system for constructing virtual creatures with
rlm@511	235 physiologically plausible sensorimotor systems and detailed
rlm@511	236 environments. The result is =CORTEX=, which is described in section
rlm@517	237 \ref{sec-2}.
rlm@511	238
rlm@511	239 Next, I wrote routines which enabled a simple worm-like creature to
rlm@511	240 infer the actions of a second worm-like creature, using only its
rlm@511	241 own prior sensorimotor experiences and knowledge of the second
rlm@511	242 worm's joint positions. This program, =EMPATH=, is described in
rlm@517	243 section \ref{sec-3}. It's main components are:
rlm@517	244
rlm@517	245 - Embodied Action Definitions :: Many otherwise complicated actions
rlm@517	246 are easily described in the language of a full suite of
rlm@517	247 body-centered, rich senses and experiences. For example,
rlm@448	248 drinking is the feeling of water sliding down your throat, and
rlm@448	249 cooling your insides. It's often accompanied by bringing your
rlm@448	250 hand close to your face, or bringing your face close to water.
rlm@448	251 Sitting down is the feeling of bending your knees, activating
rlm@448	252 your quadriceps, then feeling a surface with your bottom and
rlm@448	253 relaxing your legs. These body-centered action descriptions
rlm@448	254 can be either learned or hard coded.
rlm@517	255
rlm@517	256 - Guided Play :: The creature moves around and experiences the
rlm@517	257 world through its unique perspective. As the creature moves,
rlm@517	258 it gathers experiences that satisfy the embodied action
rlm@517	259 definitions.
rlm@517	260
rlm@517	261 - Posture imitation :: When trying to interpret a video or image,
rlm@448	262 the creature takes a model of itself and aligns it with
rlm@517	263 whatever it sees. This alignment might even cross species, as
rlm@448	264 when humans try to align themselves with things like ponies,
rlm@448	265 dogs, or other humans with a different body type.
rlm@517	266
rlm@517	267 - Empathy :: The alignment triggers associations with
rlm@448	268 sensory data from prior experiences. For example, the
rlm@448	269 alignment itself easily maps to proprioceptive data. Any
rlm@448	270 sounds or obvious skin contact in the video can to a lesser
rlm@517	271 extent trigger previous experience keyed to hearing or touch.
rlm@517	272 Segments of previous experiences gained from play are stitched
rlm@517	273 together to form a coherent and complete sensory portrait of
rlm@517	274 the scene.
rlm@517	275
rlm@547	276 - Recognition :: With the scene described in terms of remembered
rlm@547	277 first person sensory events, the creature can now run its
rlm@547	278 action-definition programs (such as the one in listing
rlm@547	279 \ref{grand-circle-intro}) on this synthesized sensory data,
rlm@517	280 just as it would if it were actually experiencing the scene
rlm@517	281 first-hand. If previous experience has been accurately
rlm@447	282 retrieved, and if it is analogous enough to the scene, then
rlm@447	283 the creature will correctly identify the action in the scene.
rlm@441	284
rlm@441	285 My program, =EMPATH= uses this empathic problem solving technique
rlm@441	286 to interpret the actions of a simple, worm-like creature.
rlm@437	287
rlm@441	288 #+caption: The worm performs many actions during free play such as
rlm@441	289 #+caption: curling, wiggling, and resting.
rlm@441	290 #+name: worm-intro
rlm@446	291 #+ATTR_LaTeX: :width 15cm
rlm@445	292 [[./images/worm-intro-white.png]]
rlm@437	293
rlm@462	294 #+caption: =EMPATH= recognized and classified each of these
rlm@462	295 #+caption: poses by inferring the complete sensory experience
rlm@462	296 #+caption: from proprioceptive data.
rlm@441	297 #+name: worm-recognition-intro
rlm@446	298 #+ATTR_LaTeX: :width 15cm
rlm@445	299 [[./images/worm-poses.png]]
rlm@441	300
rlm@517	301 *** Main Results
rlm@517	302
rlm@521	303 - After one-shot supervised training, =EMPATH= was able to
rlm@521	304 recognize a wide variety of static poses and dynamic
rlm@521	305 actions---ranging from curling in a circle to wiggling with a
rlm@521	306 particular frequency --- with 95\% accuracy.
rlm@517	307
rlm@517	308 - These results were completely independent of viewing angle
rlm@517	309 because the underlying body-centered language fundamentally is
rlm@517	310 independent; once an action is learned, it can be recognized
rlm@517	311 equally well from any viewing angle.
rlm@517	312
rlm@517	313 - =EMPATH= is surprisingly short; the sensorimotor-centered
rlm@517	314 language provided by =CORTEX= resulted in extremely economical
rlm@517	315 recognition routines --- about 500 lines in all --- suggesting
rlm@517	316 that such representations are very powerful, and often
rlm@517	317 indispensable for the types of recognition tasks considered here.
rlm@517	318
rlm@517	319 - Although for expediency's sake, I relied on direct knowledge of
rlm@517	320 joint positions in this proof of concept, it would be
rlm@517	321 straightforward to extend =EMPATH= so that it (more
rlm@517	322 realistically) infers joint positions from its visual data.
rlm@517	323
rlm@517	324 ** =EMPATH= is built on =CORTEX=, a creature builder.
rlm@435	325
rlm@448	326 I built =CORTEX= to be a general AI research platform for doing
rlm@448	327 experiments involving multiple rich senses and a wide variety and
rlm@448	328 number of creatures. I intend it to be useful as a library for many
rlm@462	329 more projects than just this thesis. =CORTEX= was necessary to meet
rlm@462	330 a need among AI researchers at CSAIL and beyond, which is that
rlm@547	331 people often will invent wonderful ideas that are best expressed in
rlm@547	332 the language of creatures and senses, but in order to explore those
rlm@448	333 ideas they must first build a platform in which they can create
rlm@448	334 simulated creatures with rich senses! There are many ideas that
rlm@547	335 would be simple to execute (such as =EMPATH= or Larson's
rlm@547	336 self-organizing maps (\cite{larson-symbols})), but attached to them
rlm@547	337 is the multi-month effort to make a good creature simulator. Often,
rlm@547	338 that initial investment of time proves to be too much, and the
rlm@547	339 project must make do with a lesser environment or be abandoned
rlm@547	340 entirely.
rlm@435	341
rlm@448	342 =CORTEX= is well suited as an environment for embodied AI research
rlm@448	343 for three reasons:
rlm@448	344
rlm@547	345 - You can design new creatures using Blender (\cite{blender}), a
rlm@517	346 popular 3D modeling program. Each sense can be specified using
rlm@517	347 special blender nodes with biologically inspired parameters. You
rlm@517	348 need not write any code to create a creature, and can use a wide
rlm@517	349 library of pre-existing blender models as a base for your own
rlm@517	350 creatures.
rlm@448	351
rlm@511	352 - =CORTEX= implements a wide variety of senses: touch,
rlm@448	353 proprioception, vision, hearing, and muscle tension. Complicated
rlm@545	354 senses like touch and vision involve multiple sensory elements
rlm@448	355 embedded in a 2D surface. You have complete control over the
rlm@448	356 distribution of these sensor elements through the use of simple
rlm@547	357 png image files. =CORTEX= implements more comprehensive hearing
rlm@547	358 than any other creature simulation system available.
rlm@448	359
rlm@448	360 - =CORTEX= supports any number of creatures and any number of
rlm@517	361 senses. Time in =CORTEX= dilates so that the simulated creatures
rlm@517	362 always perceive a perfectly smooth flow of time, regardless of
rlm@448	363 the actual computational load.
rlm@448	364
rlm@517	365 =CORTEX= is built on top of =jMonkeyEngine3=
rlm@517	366 (\cite{jmonkeyengine}), which is a video game engine designed to
rlm@517	367 create cross-platform 3D desktop games. =CORTEX= is mainly written
rlm@517	368 in clojure, a dialect of =LISP= that runs on the java virtual
rlm@517	369 machine (JVM). The API for creating and simulating creatures and
rlm@517	370 senses is entirely expressed in clojure, though many senses are
rlm@517	371 implemented at the layer of jMonkeyEngine or below. For example,
rlm@517	372 for the sense of hearing I use a layer of clojure code on top of a
rlm@517	373 layer of java JNI bindings that drive a layer of =C++= code which
rlm@517	374 implements a modified version of =OpenAL= to support multiple
rlm@517	375 listeners. =CORTEX= is the only simulation environment that I know
rlm@517	376 of that can support multiple entities that can each hear the world
rlm@517	377 from their own perspective. Other senses also require a small layer
rlm@517	378 of Java code. =CORTEX= also uses =bullet=, a physics simulator
rlm@517	379 written in =C=.
rlm@448	380
rlm@516	381 #+caption: Here is the worm from figure \ref{worm-intro} modeled
rlm@516	382 #+caption: in Blender, a free 3D-modeling program. Senses and
rlm@516	383 #+caption: joints are described using special nodes in Blender.
rlm@518	384 #+name: worm-recognition-intro-2
rlm@448	385 #+ATTR_LaTeX: :width 12cm
rlm@448	386 [[./images/blender-worm.png]]
rlm@448	387
rlm@521	388 Here are some things I anticipate that =CORTEX= might be used for:
rlm@449	389
rlm@449	390 - exploring new ideas about sensory integration
rlm@449	391 - distributed communication among swarm creatures
rlm@449	392 - self-learning using free exploration,
rlm@449	393 - evolutionary algorithms involving creature construction
rlm@517	394 - exploration of exotic senses and effectors that are not possible
rlm@517	395 in the real world (such as telekinesis or a semantic sense)
rlm@449	396 - imagination using subworlds
rlm@449	397
rlm@451	398 During one test with =CORTEX=, I created 3,000 creatures each with
rlm@448	399 their own independent senses and ran them all at only 1/80 real
rlm@448	400 time. In another test, I created a detailed model of my own hand,
rlm@448	401 equipped with a realistic distribution of touch (more sensitive at
rlm@448	402 the fingertips), as well as eyes and ears, and it ran at around 1/4
rlm@451	403 real time.
rlm@448	404
rlm@451	405 #+BEGIN_LaTeX
rlm@449	406 \begin{sidewaysfigure}
rlm@449	407 \includegraphics[width=9.5in]{images/full-hand.png}
rlm@451	408 \caption{
rlm@451	409 I modeled my own right hand in Blender and rigged it with all the
rlm@451	410 senses that {\tt CORTEX} supports. My simulated hand has a
rlm@451	411 biologically inspired distribution of touch sensors. The senses are
rlm@451	412 displayed on the right, and the simulation is displayed on the
rlm@451	413 left. Notice that my hand is curling its fingers, that it can see
rlm@451	414 its own finger from the eye in its palm, and that it can feel its
rlm@451	415 own thumb touching its palm.}
rlm@449	416 \end{sidewaysfigure}
rlm@451	417 #+END_LaTeX
rlm@448	418
rlm@541	419 * Designing =CORTEX=
rlm@516	420
rlm@511	421 In this section, I outline the design decisions that went into
rlm@516	422 making =CORTEX=, along with some details about its implementation.
rlm@516	423 (A practical guide to getting started with =CORTEX=, which skips
rlm@516	424 over the history and implementation details presented here, is
rlm@516	425 provided in an appendix at the end of this thesis.)
rlm@511	426
rlm@511	427 Throughout this project, I intended for =CORTEX= to be flexible and
rlm@511	428 extensible enough to be useful for other researchers who want to
rlm@547	429 test ideas of their own. To this end, wherever I have had to make
rlm@517	430 architectural choices about =CORTEX=, I have chosen to give as much
rlm@511	431 freedom to the user as possible, so that =CORTEX= may be used for
rlm@517	432 things I have not foreseen.
rlm@511	433
rlm@511	434 ** Building in simulation versus reality
rlm@517	435 The most important architectural decision of all is the choice to
rlm@517	436 use a computer-simulated environment in the first place! The world
rlm@462	437 is a vast and rich place, and for now simulations are a very poor
rlm@462	438 reflection of its complexity. It may be that there is a significant
rlm@517	439 qualitative difference between dealing with senses in the real
rlm@517	440 world and dealing with pale facsimiles of them in a simulation
rlm@547	441 (\cite{brooks-representation}). What are the advantages and
rlm@514	442 disadvantages of a simulation vs. reality?
rlm@515	443
rlm@462	444 *** Simulation
rlm@462	445
rlm@462	446 The advantages of virtual reality are that when everything is a
rlm@462	447 simulation, experiments in that simulation are absolutely
rlm@547	448 reproducible. It's also easier to change the creature and
rlm@547	449 environment to explore new situations and different sensory
rlm@547	450 combinations.
rlm@462	451
rlm@462	452 If the world is to be simulated on a computer, then not only do
rlm@547	453 you have to worry about whether the creature's senses are rich
rlm@462	454 enough to learn from the world, but whether the world itself is
rlm@462	455 rendered with enough detail and realism to give enough working
rlm@547	456 material to the creature's senses. To name just a few
rlm@462	457 difficulties facing modern physics simulators: destructibility of
rlm@462	458 the environment, simulation of water/other fluids, large areas,
rlm@462	459 nonrigid bodies, lots of objects, smoke. I don't know of any
rlm@547	460 computer simulation that would allow a creature to take a rock
rlm@462	461 and grind it into fine dust, then use that dust to make a clay
rlm@462	462 sculpture, at least not without spending years calculating the
rlm@462	463 interactions of every single small grain of dust. Maybe a
rlm@462	464 simulated world with today's limitations doesn't provide enough
rlm@462	465 richness for real intelligence to evolve.
rlm@462	466
rlm@462	467 *** Reality
rlm@462	468
rlm@462	469 The other approach for playing with senses is to hook your
rlm@462	470 software up to real cameras, microphones, robots, etc., and let it
rlm@462	471 loose in the real world. This has the advantage of eliminating
rlm@462	472 concerns about simulating the world at the expense of increasing
rlm@462	473 the complexity of implementing the senses. Instead of just
rlm@462	474 grabbing the current rendered frame for processing, you have to
rlm@462	475 use an actual camera with real lenses and interact with photons to
rlm@547	476 get an image. It is much harder to change the creature, which is
rlm@462	477 now partly a physical robot of some sort, since doing so involves
rlm@462	478 changing things around in the real world instead of modifying
rlm@462	479 lines of code. While the real world is very rich and definitely
rlm@547	480 provides enough stimulation for intelligence to develop (as
rlm@547	481 evidenced by our own existence), it is also uncontrollable in the
rlm@462	482 sense that a particular situation cannot be recreated perfectly or
rlm@547	483 saved for later use. It is harder to conduct Science because it is
rlm@462	484 harder to repeat an experiment. The worst thing about using the
rlm@462	485 real world instead of a simulation is the matter of time. Instead
rlm@462	486 of simulated time you get the constant and unstoppable flow of
rlm@462	487 real time. This severely limits the sorts of software you can use
rlm@525	488 to program an AI, because all sense inputs must be handled in real
rlm@462	489 time. Complicated ideas may have to be implemented in hardware or
rlm@462	490 may simply be impossible given the current speed of our
rlm@462	491 processors. Contrast this with a simulation, in which the flow of
rlm@462	492 time in the simulated world can be slowed down to accommodate the
rlm@547	493 limitations of the creature's programming. In terms of cost, doing
rlm@547	494 everything in software is far cheaper than building custom
rlm@462	495 real-time hardware. All you need is a laptop and some patience.
rlm@515	496
rlm@516	497 ** Simulated time enables rapid prototyping \& simple programs
rlm@435	498
rlm@462	499 I envision =CORTEX= being used to support rapid prototyping and
rlm@462	500 iteration of ideas. Even if I could put together a well constructed
rlm@462	501 kit for creating robots, it would still not be enough because of
rlm@462	502 the scourge of real-time processing. Anyone who wants to test their
rlm@462	503 ideas in the real world must always worry about getting their
rlm@465	504 algorithms to run fast enough to process information in real time.
rlm@465	505 The need for real time processing only increases if multiple senses
rlm@465	506 are involved. In the extreme case, even simple algorithms will have
rlm@465	507 to be accelerated by ASIC chips or FPGAs, turning what would
rlm@517	508 otherwise be a few lines of code and a 10x speed penalty into a
rlm@465	509 multi-month ordeal. For this reason, =CORTEX= supports
rlm@547	510 /time-dilation/, which scales back the framerate of the simulation
rlm@547	511 in proportion to the amount of processing each frame. From the
rlm@547	512 perspective of the creatures inside the simulation, time always
rlm@547	513 appears to flow at a constant rate, regardless of how complicated
rlm@547	514 the environment becomes or how many creatures are in the
rlm@547	515 simulation. The cost is that =CORTEX= can sometimes run slower than
rlm@547	516 real time. Time dialation works both ways, however --- simulations
rlm@547	517 of very simple creatures in =CORTEX= generally run at 40x real-time
rlm@547	518 on my machine!
rlm@462	519
rlm@511	520 ** All sense organs are two-dimensional surfaces
rlm@514	521
rlm@468	522 If =CORTEX= is to support a wide variety of senses, it would help
rlm@547	523 to have a better understanding of what a sense actually is! While
rlm@547	524 vision, touch, and hearing all seem like they are quite different
rlm@547	525 things, I was surprised to learn during the course of this thesis
rlm@547	526 that they (and all physical senses) can be expressed as exactly the
rlm@547	527 same mathematical object!
rlm@468	528
rlm@468	529 Human beings are three-dimensional objects, and the nerves that
rlm@468	530 transmit data from our various sense organs to our brain are
rlm@468	531 essentially one-dimensional. This leaves up to two dimensions in
rlm@468	532 which our sensory information may flow. For example, imagine your
rlm@468	533 skin: it is a two-dimensional surface around a three-dimensional
rlm@468	534 object (your body). It has discrete touch sensors embedded at
rlm@468	535 various points, and the density of these sensors corresponds to the
rlm@468	536 sensitivity of that region of skin. Each touch sensor connects to a
rlm@468	537 nerve, all of which eventually are bundled together as they travel
rlm@468	538 up the spinal cord to the brain. Intersect the spinal nerves with a
rlm@468	539 guillotining plane and you will see all of the sensory data of the
rlm@468	540 skin revealed in a roughly circular two-dimensional image which is
rlm@468	541 the cross section of the spinal cord. Points on this image that are
rlm@468	542 close together in this circle represent touch sensors that are
rlm@468	543 /probably/ close together on the skin, although there is of course
rlm@468	544 some cutting and rearrangement that has to be done to transfer the
rlm@468	545 complicated surface of the skin onto a two dimensional image.
rlm@468	546
rlm@468	547 Most human senses consist of many discrete sensors of various
rlm@468	548 properties distributed along a surface at various densities. For
rlm@468	549 skin, it is Pacinian corpuscles, Meissner's corpuscles, Merkel's
rlm@547	550 disks, and Ruffini's endings (\cite{textbook901}), which detect
rlm@517	551 pressure and vibration of various intensities. For ears, it is the
rlm@517	552 stereocilia distributed along the basilar membrane inside the
rlm@517	553 cochlea; each one is sensitive to a slightly different frequency of
rlm@517	554 sound. For eyes, it is rods and cones distributed along the surface
rlm@517	555 of the retina. In each case, we can describe the sense with a
rlm@517	556 surface and a distribution of sensors along that surface.
rlm@468	557
rlm@525	558 In fact, almost every human sense can be effectively described in
rlm@525	559 terms of a surface containing embedded sensors. If the sense had
rlm@525	560 any more dimensions, then there wouldn't be enough room in the
rlm@547	561 spinal cord to transmit the information!
rlm@468	562
rlm@468	563 Therefore, =CORTEX= must support the ability to create objects and
rlm@468	564 then be able to ``paint'' points along their surfaces to describe
rlm@468	565 each sense.
rlm@468	566
rlm@468	567 Fortunately this idea is already a well known computer graphics
rlm@547	568 technique called /UV-mapping/. In UV-maping, the three-dimensional
rlm@547	569 surface of a model is cut and smooshed until it fits on a
rlm@547	570 two-dimensional image. You paint whatever you want on that image,
rlm@547	571 and when the three-dimensional shape is rendered in a game the
rlm@547	572 smooshing and cutting is reversed and the image appears on the
rlm@547	573 three-dimensional object.
rlm@468	574
rlm@468	575 To make a sense, interpret the UV-image as describing the
rlm@468	576 distribution of that senses sensors. To get different types of
rlm@468	577 sensors, you can either use a different color for each type of
rlm@468	578 sensor, or use multiple UV-maps, each labeled with that sensor
rlm@468	579 type. I generally use a white pixel to mean the presence of a
rlm@468	580 sensor and a black pixel to mean the absence of a sensor, and use
rlm@468	581 one UV-map for each sensor-type within a given sense.
rlm@468	582
rlm@468	583 #+CAPTION: The UV-map for an elongated icososphere. The white
rlm@468	584 #+caption: dots each represent a touch sensor. They are dense
rlm@468	585 #+caption: in the regions that describe the tip of the finger,
rlm@468	586 #+caption: and less dense along the dorsal side of the finger
rlm@468	587 #+caption: opposite the tip.
rlm@468	588 #+name: finger-UV
rlm@468	589 #+ATTR_latex: :width 10cm
rlm@468	590 [[./images/finger-UV.png]]
rlm@468	591
rlm@468	592 #+caption: Ventral side of the UV-mapped finger. Notice the
rlm@468	593 #+caption: density of touch sensors at the tip.
rlm@468	594 #+name: finger-side-view
rlm@468	595 #+ATTR_LaTeX: :width 10cm
rlm@468	596 [[./images/finger-1.png]]
rlm@468	597
rlm@507	598 ** Video game engines provide ready-made physics and shading
rlm@462	599
rlm@462	600 I did not need to write my own physics simulation code or shader to
rlm@462	601 build =CORTEX=. Doing so would lead to a system that is impossible
rlm@462	602 for anyone but myself to use anyway. Instead, I use a video game
rlm@517	603 engine as a base and modify it to accommodate the additional needs
rlm@462	604 of =CORTEX=. Video game engines are an ideal starting point to
rlm@462	605 build =CORTEX=, because they are not far from being creature
rlm@463	606 building systems themselves.
rlm@462	607
rlm@462	608 First off, general purpose video game engines come with a physics
rlm@462	609 engine and lighting / sound system. The physics system provides
rlm@462	610 tools that can be co-opted to serve as touch, proprioception, and
rlm@462	611 muscles. Since some games support split screen views, a good video
rlm@462	612 game engine will allow you to efficiently create multiple cameras
rlm@463	613 in the simulated world that can be used as eyes. Video game systems
rlm@463	614 offer integrated asset management for things like textures and
rlm@547	615 creature models, providing an avenue for defining creatures. They
rlm@468	616 also understand UV-mapping, since this technique is used to apply a
rlm@468	617 texture to a model. Finally, because video game engines support a
rlm@547	618 large number of developers, as long as =CORTEX= doesn't stray too
rlm@547	619 far from the base system, other researchers can turn to this
rlm@547	620 community for help when doing their research.
rlm@463	621
rlm@507	622 ** =CORTEX= is based on jMonkeyEngine3
rlm@463	623
rlm@463	624 While preparing to build =CORTEX= I studied several video game
rlm@463	625 engines to see which would best serve as a base. The top contenders
rlm@463	626 were:
rlm@463	627
rlm@547	628 - [[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	629 in 1997. All the source code was released by ID software into
rlm@547	630 the Public Domain several years ago, and as a result it has
rlm@547	631 been ported to many different languages. This engine was
rlm@547	632 famous for its advanced use of realistic shading and it had
rlm@547	633 decent and fast physics simulation. The main advantage of the
rlm@547	634 Quake II engine is its simplicity, but I ultimately rejected
rlm@547	635 it because the engine is too tied to the concept of a
rlm@463	636 first-person shooter game. One of the problems I had was that
rlm@463	637 there does not seem to be any easy way to attach multiple
rlm@463	638 cameras to a single character. There are also several physics
rlm@463	639 clipping issues that are corrected in a way that only applies
rlm@463	640 to the main character and do not apply to arbitrary objects.
rlm@463	641
rlm@463	642 - [[http://source.valvesoftware.com/][Source Engine]] :: The Source Engine evolved from the Quake II
rlm@463	643 and Quake I engines and is used by Valve in the Half-Life
rlm@463	644 series of games. The physics simulation in the Source Engine
rlm@463	645 is quite accurate and probably the best out of all the engines
rlm@463	646 I investigated. There is also an extensive community actively
rlm@463	647 working with the engine. However, applications that use the
rlm@463	648 Source Engine must be written in C++, the code is not open, it
rlm@463	649 only runs on Windows, and the tools that come with the SDK to
rlm@463	650 handle models and textures are complicated and awkward to use.
rlm@463	651
rlm@463	652 - [[http://jmonkeyengine.com/][jMonkeyEngine3]] :: jMonkeyEngine3 is a new library for creating
rlm@463	653 games in Java. It uses OpenGL to render to the screen and uses
rlm@463	654 screengraphs to avoid drawing things that do not appear on the
rlm@463	655 screen. It has an active community and several games in the
rlm@463	656 pipeline. The engine was not built to serve any particular
rlm@463	657 game but is instead meant to be used for any 3D game.
rlm@463	658
rlm@521	659 I chose jMonkeyEngine3 because it had the most features out of all
rlm@521	660 the free projects I looked at, and because I could then write my
rlm@521	661 code in clojure, an implementation of =LISP= that runs on the JVM.
rlm@435	662
rlm@507	663 ** =CORTEX= uses Blender to create creature models
rlm@435	664
rlm@464	665 For the simple worm-like creatures I will use later on in this
rlm@464	666 thesis, I could define a simple API in =CORTEX= that would allow
rlm@464	667 one to create boxes, spheres, etc., and leave that API as the sole
rlm@464	668 way to create creatures. However, for =CORTEX= to truly be useful
rlm@468	669 for other projects, it needs a way to construct complicated
rlm@464	670 creatures. If possible, it would be nice to leverage work that has
rlm@464	671 already been done by the community of 3D modelers, or at least
rlm@517	672 enable people who are talented at modeling but not programming to
rlm@468	673 design =CORTEX= creatures.
rlm@464	674
rlm@547	675 Therefore I use Blender, a free 3D modeling program, as the main
rlm@464	676 way to create creatures in =CORTEX=. However, the creatures modeled
rlm@464	677 in Blender must also be simple to simulate in jMonkeyEngine3's game
rlm@468	678 engine, and must also be easy to rig with =CORTEX='s senses. I
rlm@547	679 accomplish this with extensive use of Blender's ``empty nodes.''
rlm@464	680
rlm@468	681 Empty nodes have no mass, physical presence, or appearance, but
rlm@468	682 they can hold metadata and have names. I use a tree structure of
rlm@468	683 empty nodes to specify senses in the following manner:
rlm@468	684
rlm@468	685 - Create a single top-level empty node whose name is the name of
rlm@468	686 the sense.
rlm@468	687 - Add empty nodes which each contain meta-data relevant to the
rlm@468	688 sense, including a UV-map describing the number/distribution of
rlm@468	689 sensors if applicable.
rlm@468	690 - Make each empty-node the child of the top-level node.
rlm@468	691
rlm@517	692 #+caption: An example of annotating a creature model with empty
rlm@468	693 #+caption: nodes to describe the layout of senses. There are
rlm@468	694 #+caption: multiple empty nodes which each describe the position
rlm@468	695 #+caption: of muscles, ears, eyes, or joints.
rlm@468	696 #+name: sense-nodes
rlm@468	697 #+ATTR_LaTeX: :width 10cm
rlm@468	698 [[./images/empty-sense-nodes.png]]
rlm@468	699
rlm@508	700 ** Bodies are composed of segments connected by joints
rlm@468	701
rlm@468	702 Blender is a general purpose animation tool, which has been used in
rlm@468	703 the past to create high quality movies such as Sintel
rlm@547	704 (\cite{blender}). Though Blender can model and render even
rlm@547	705 complicated things like water, it is crucial to keep models that
rlm@547	706 are meant to be simulated as creatures simple. =Bullet=, which
rlm@547	707 =CORTEX= uses though jMonkeyEngine3, is a rigid-body physics
rlm@547	708 system. This offers a compromise between the expressiveness of a
rlm@547	709 game level and the speed at which it can be simulated, and it means
rlm@547	710 that creatures should be naturally expressed as rigid components
rlm@547	711 held together by joint constraints.
rlm@468	712
rlm@517	713 But humans are more like a squishy bag wrapped around some hard
rlm@517	714 bones which define the overall shape. When we move, our skin bends
rlm@517	715 and stretches to accommodate the new positions of our bones.
rlm@468	716
rlm@468	717 One way to make bodies composed of rigid pieces connected by joints
rlm@468	718 /seem/ more human-like is to use an /armature/, (or /rigging/)
rlm@468	719 system, which defines a overall ``body mesh'' and defines how the
rlm@468	720 mesh deforms as a function of the position of each ``bone'' which
rlm@468	721 is a standard rigid body. This technique is used extensively to
rlm@468	722 model humans and create realistic animations. It is not a good
rlm@517	723 technique for physical simulation because it is a lie -- the skin
rlm@517	724 is not a physical part of the simulation and does not interact with
rlm@517	725 any objects in the world or itself. Objects will pass right though
rlm@517	726 the skin until they come in contact with the underlying bone, which
rlm@517	727 is a physical object. Without simulating the skin, the sense of
rlm@517	728 touch has little meaning, and the creature's own vision will lie to
rlm@517	729 it about the true extent of its body. Simulating the skin as a
rlm@517	730 physical object requires some way to continuously update the
rlm@517	731 physical model of the skin along with the movement of the bones,
rlm@517	732 which is unacceptably slow compared to rigid body simulation.
rlm@468	733
rlm@547	734 Therefore, instead of using the human-like ``bony meatbag''
rlm@547	735 approach, I decided to base my body plans on multiple solid objects
rlm@547	736 that are connected by joints, inspired by the robot =EVE= from the
rlm@547	737 movie WALL-E.
rlm@464	738
rlm@464	739 #+caption: =EVE= from the movie WALL-E. This body plan turns
rlm@464	740 #+caption: out to be much better suited to my purposes than a more
rlm@464	741 #+caption: human-like one.
rlm@465	742 #+ATTR_LaTeX: :width 10cm
rlm@464	743 [[./images/Eve.jpg]]
rlm@464	744
rlm@464	745 =EVE='s body is composed of several rigid components that are held
rlm@464	746 together by invisible joint constraints. This is what I mean by
rlm@547	747 /eve-like/. The main reason that I use eve-like bodies is for
rlm@547	748 simulation efficiency, and so that there will be correspondence
rlm@547	749 between the AI's senses and the physical presence of its body. Each
rlm@547	750 individual section is simulated by a separate rigid body that
rlm@547	751 corresponds exactly with its visual representation and does not
rlm@547	752 change. Sections are connected by invisible joints that are well
rlm@547	753 supported in jMonkeyEngine3. Bullet, the physics backend for
rlm@547	754 jMonkeyEngine3, can efficiently simulate hundreds of rigid bodies
rlm@547	755 connected by joints. Just because sections are rigid does not mean
rlm@547	756 they have to stay as one piece forever; they can be dynamically
rlm@547	757 replaced with multiple sections to simulate splitting in two. This
rlm@547	758 could be used to simulate retractable claws or =EVE='s hands, which
rlm@547	759 are able to coalesce into one object in the movie.
rlm@465	760
rlm@469	761 *** Solidifying/Connecting a body
rlm@465	762
rlm@469	763 =CORTEX= creates a creature in two steps: first, it traverses the
rlm@469	764 nodes in the blender file and creates physical representations for
rlm@469	765 any of them that have mass defined in their blender meta-data.
rlm@466	766
rlm@466	767 #+caption: Program for iterating through the nodes in a blender file
rlm@466	768 #+caption: and generating physical jMonkeyEngine3 objects with mass
rlm@466	769 #+caption: and a matching physics shape.
rlm@518	770 #+name: physical
rlm@466	771 #+begin_listing clojure
rlm@466	772 #+begin_src clojure
rlm@466	773 (defn physical!
rlm@466	774 "Iterate through the nodes in creature and make them real physical
rlm@466	775 objects in the simulation."
rlm@466	776 [#^Node creature]
rlm@466	777 (dorun
rlm@466	778 (map
rlm@466	779 (fn [geom]
rlm@466	780 (let [physics-control
rlm@466	781 (RigidBodyControl.
rlm@466	782 (HullCollisionShape.
rlm@466	783 (.getMesh geom))
rlm@466	784 (if-let [mass (meta-data geom "mass")]
rlm@466	785 (float mass) (float 1)))]
rlm@466	786 (.addControl geom physics-control)))
rlm@466	787 (filter #(isa? (class %) Geometry )
rlm@466	788 (node-seq creature)))))
rlm@466	789 #+end_src
rlm@466	790 #+end_listing
rlm@465	791
rlm@469	792 The next step to making a proper body is to connect those pieces
rlm@469	793 together with joints. jMonkeyEngine has a large array of joints
rlm@469	794 available via =bullet=, such as Point2Point, Cone, Hinge, and a
rlm@469	795 generic Six Degree of Freedom joint, with or without spring
rlm@469	796 restitution.
rlm@465	797
rlm@469	798 Joints are treated a lot like proper senses, in that there is a
rlm@469	799 top-level empty node named ``joints'' whose children each
rlm@469	800 represent a joint.
rlm@466	801
rlm@469	802 #+caption: View of the hand model in Blender showing the main ``joints''
rlm@469	803 #+caption: node (highlighted in yellow) and its children which each
rlm@469	804 #+caption: represent a joint in the hand. Each joint node has metadata
rlm@469	805 #+caption: specifying what sort of joint it is.
rlm@469	806 #+name: blender-hand
rlm@469	807 #+ATTR_LaTeX: :width 10cm
rlm@469	808 [[./images/hand-screenshot1.png]]
rlm@469	809
rlm@469	810
rlm@469	811 =CORTEX='s procedure for binding the creature together with joints
rlm@469	812 is as follows:
rlm@469	813
rlm@469	814 - Find the children of the ``joints'' node.
rlm@469	815 - Determine the two spatials the joint is meant to connect.
rlm@469	816 - Create the joint based on the meta-data of the empty node.
rlm@469	817
rlm@469	818 The higher order function =sense-nodes= from =cortex.sense=
rlm@469	819 simplifies finding the joints based on their parent ``joints''
rlm@469	820 node.
rlm@466	821
rlm@466	822 #+caption: Retrieving the children empty nodes from a single
rlm@466	823 #+caption: named empty node is a common pattern in =CORTEX=
rlm@466	824 #+caption: further instances of this technique for the senses
rlm@466	825 #+caption: will be omitted
rlm@466	826 #+name: get-empty-nodes
rlm@466	827 #+begin_listing clojure
rlm@466	828 #+begin_src clojure
rlm@466	829 (defn sense-nodes
rlm@466	830 "For some senses there is a special empty blender node whose
rlm@466	831 children are considered markers for an instance of that sense. This
rlm@466	832 function generates functions to find those children, given the name
rlm@466	833 of the special parent node."
rlm@466	834 [parent-name]
rlm@466	835 (fn [#^Node creature]
rlm@466	836 (if-let [sense-node (.getChild creature parent-name)]
rlm@466	837 (seq (.getChildren sense-node)) [])))
rlm@466	838
rlm@466	839 (def
rlm@466	840 ^{:doc "Return the children of the creature's \"joints\" node."
rlm@466	841 :arglists '([creature])}
rlm@466	842 joints
rlm@466	843 (sense-nodes "joints"))
rlm@466	844 #+end_src
rlm@466	845 #+end_listing
rlm@466	846
rlm@469	847 To find a joint's targets, =CORTEX= creates a small cube, centered
rlm@469	848 around the empty-node, and grows the cube exponentially until it
rlm@469	849 intersects two physical objects. The objects are ordered according
rlm@469	850 to the joint's rotation, with the first one being the object that
rlm@469	851 has more negative coordinates in the joint's reference frame.
rlm@469	852 Since the objects must be physical, the empty-node itself escapes
rlm@469	853 detection. Because the objects must be physical, =joint-targets=
rlm@469	854 must be called /after/ =physical!= is called.
rlm@464	855
rlm@469	856 #+caption: Program to find the targets of a joint node by
rlm@517	857 #+caption: exponentially growth of a search cube.
rlm@469	858 #+name: joint-targets
rlm@469	859 #+begin_listing clojure
rlm@469	860 #+begin_src clojure
rlm@466	861 (defn joint-targets
rlm@466	862 "Return the two closest two objects to the joint object, ordered
rlm@466	863 from bottom to top according to the joint's rotation."
rlm@466	864 [#^Node parts #^Node joint]
rlm@466	865 (loop [radius (float 0.01)]
rlm@466	866 (let [results (CollisionResults.)]
rlm@466	867 (.collideWith
rlm@466	868 parts
rlm@466	869 (BoundingBox. (.getWorldTranslation joint)
rlm@466	870 radius radius radius) results)
rlm@466	871 (let [targets
rlm@466	872 (distinct
rlm@466	873 (map #(.getGeometry %) results))]
rlm@466	874 (if (>= (count targets) 2)
rlm@466	875 (sort-by
rlm@466	876 #(let [joint-ref-frame-position
rlm@466	877 (jme-to-blender
rlm@466	878 (.mult
rlm@466	879 (.inverse (.getWorldRotation joint))
rlm@466	880 (.subtract (.getWorldTranslation %)
rlm@466	881 (.getWorldTranslation joint))))]
rlm@466	882 (.dot (Vector3f. 1 1 1) joint-ref-frame-position))
rlm@466	883 (take 2 targets))
rlm@466	884 (recur (float (* radius 2))))))))
rlm@469	885 #+end_src
rlm@469	886 #+end_listing
rlm@464	887
rlm@469	888 Once =CORTEX= finds all joints and targets, it creates them using
rlm@469	889 a dispatch on the metadata of each joint node.
rlm@466	890
rlm@469	891 #+caption: Program to dispatch on blender metadata and create joints
rlm@517	892 #+caption: suitable for physical simulation.
rlm@469	893 #+name: joint-dispatch
rlm@469	894 #+begin_listing clojure
rlm@469	895 #+begin_src clojure
rlm@466	896 (defmulti joint-dispatch
rlm@466	897 "Translate blender pseudo-joints into real JME joints."
rlm@466	898 (fn [constraints & _]
rlm@466	899 (:type constraints)))
rlm@466	900
rlm@466	901 (defmethod joint-dispatch :point
rlm@466	902 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466	903 (doto (SixDofJoint. control-a control-b pivot-a pivot-b false)
rlm@466	904 (.setLinearLowerLimit Vector3f/ZERO)
rlm@466	905 (.setLinearUpperLimit Vector3f/ZERO)))
rlm@466	906
rlm@466	907 (defmethod joint-dispatch :hinge
rlm@466	908 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466	909 (let [axis (if-let [axis (:axis constraints)] axis Vector3f/UNIT_X)
rlm@466	910 [limit-1 limit-2] (:limit constraints)
rlm@466	911 hinge-axis (.mult rotation (blender-to-jme axis))]
rlm@466	912 (doto (HingeJoint. control-a control-b pivot-a pivot-b
rlm@466	913 hinge-axis hinge-axis)
rlm@466	914 (.setLimit limit-1 limit-2))))
rlm@466	915
rlm@466	916 (defmethod joint-dispatch :cone
rlm@466	917 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466	918 (let [limit-xz (:limit-xz constraints)
rlm@466	919 limit-xy (:limit-xy constraints)
rlm@466	920 twist (:twist constraints)]
rlm@466	921 (doto (ConeJoint. control-a control-b pivot-a pivot-b
rlm@466	922 rotation rotation)
rlm@466	923 (.setLimit (float limit-xz) (float limit-xy)
rlm@466	924 (float twist)))))
rlm@469	925 #+end_src
rlm@469	926 #+end_listing
rlm@466	927
rlm@522	928 All that is left for joints is to combine the above pieces into
rlm@469	929 something that can operate on the collection of nodes that a
rlm@469	930 blender file represents.
rlm@466	931
rlm@469	932 #+caption: Program to completely create a joint given information
rlm@469	933 #+caption: from a blender file.
rlm@469	934 #+name: connect
rlm@469	935 #+begin_listing clojure
rlm@466	936 #+begin_src clojure
rlm@466	937 (defn connect
rlm@466	938 "Create a joint between 'obj-a and 'obj-b at the location of
rlm@466	939 'joint. The type of joint is determined by the metadata on 'joint.
rlm@466	940
rlm@466	941 Here are some examples:
rlm@466	942 {:type :point}
rlm@466	943 {:type :hinge :limit [0 (/ Math/PI 2)] :axis (Vector3f. 0 1 0)}
rlm@466	944 (:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)
rlm@466	945
rlm@466	946 {:type :cone :limit-xz 0]
rlm@466	947 :limit-xy 0]
rlm@466	948 :twist 0]} (use XZY rotation mode in blender!)"
rlm@466	949 [#^Node obj-a #^Node obj-b #^Node joint]
rlm@466	950 (let [control-a (.getControl obj-a RigidBodyControl)
rlm@466	951 control-b (.getControl obj-b RigidBodyControl)
rlm@466	952 joint-center (.getWorldTranslation joint)
rlm@466	953 joint-rotation (.toRotationMatrix (.getWorldRotation joint))
rlm@466	954 pivot-a (world-to-local obj-a joint-center)
rlm@466	955 pivot-b (world-to-local obj-b joint-center)]
rlm@466	956 (if-let
rlm@466	957 [constraints (map-vals eval (read-string (meta-data joint "joint")))]
rlm@466	958 ;; A side-effect of creating a joint registers
rlm@466	959 ;; it with both physics objects which in turn
rlm@466	960 ;; will register the joint with the physics system
rlm@466	961 ;; when the simulation is started.
rlm@466	962 (joint-dispatch constraints
rlm@466	963 control-a control-b
rlm@466	964 pivot-a pivot-b
rlm@466	965 joint-rotation))))
rlm@469	966 #+end_src
rlm@469	967 #+end_listing
rlm@466	968
rlm@469	969 In general, whenever =CORTEX= exposes a sense (or in this case
rlm@469	970 physicality), it provides a function of the type =sense!=, which
rlm@469	971 takes in a collection of nodes and augments it to support that
rlm@517	972 sense. The function returns any controls necessary to use that
rlm@517	973 sense. In this case =body!= creates a physical body and returns no
rlm@469	974 control functions.
rlm@466	975
rlm@469	976 #+caption: Program to give joints to a creature.
rlm@518	977 #+name: joints
rlm@469	978 #+begin_listing clojure
rlm@469	979 #+begin_src clojure
rlm@466	980 (defn joints!
rlm@466	981 "Connect the solid parts of the creature with physical joints. The
rlm@466	982 joints are taken from the \"joints\" node in the creature."
rlm@466	983 [#^Node creature]
rlm@466	984 (dorun
rlm@466	985 (map
rlm@466	986 (fn [joint]
rlm@466	987 (let [[obj-a obj-b] (joint-targets creature joint)]
rlm@466	988 (connect obj-a obj-b joint)))
rlm@466	989 (joints creature))))
rlm@466	990 (defn body!
rlm@466	991 "Endow the creature with a physical body connected with joints. The
rlm@466	992 particulars of the joints and the masses of each body part are
rlm@466	993 determined in blender."
rlm@466	994 [#^Node creature]
rlm@466	995 (physical! creature)
rlm@466	996 (joints! creature))
rlm@469	997 #+end_src
rlm@469	998 #+end_listing
rlm@466	999
rlm@469	1000 All of the code you have just seen amounts to only 130 lines, yet
rlm@469	1001 because it builds on top of Blender and jMonkeyEngine3, those few
rlm@469	1002 lines pack quite a punch!
rlm@466	1003
rlm@469	1004 The hand from figure \ref{blender-hand}, which was modeled after
rlm@469	1005 my own right hand, can now be given joints and simulated as a
rlm@469	1006 creature.
rlm@466	1007
rlm@469	1008 #+caption: With the ability to create physical creatures from blender,
rlm@517	1009 #+caption: =CORTEX= gets one step closer to becoming a full creature
rlm@469	1010 #+caption: simulation environment.
rlm@518	1011 #+name: physical-hand
rlm@469	1012 #+ATTR_LaTeX: :width 15cm
rlm@469	1013 [[./images/physical-hand.png]]
rlm@468	1014
rlm@511	1015 ** Sight reuses standard video game components...
rlm@436	1016
rlm@470	1017 Vision is one of the most important senses for humans, so I need to
rlm@470	1018 build a simulated sense of vision for my AI. I will do this with
rlm@470	1019 simulated eyes. Each eye can be independently moved and should see
rlm@470	1020 its own version of the world depending on where it is.
rlm@470	1021
rlm@470	1022 Making these simulated eyes a reality is simple because
rlm@470	1023 jMonkeyEngine already contains extensive support for multiple views
rlm@470	1024 of the same 3D simulated world. The reason jMonkeyEngine has this
rlm@470	1025 support is because the support is necessary to create games with
rlm@470	1026 split-screen views. Multiple views are also used to create
rlm@470	1027 efficient pseudo-reflections by rendering the scene from a certain
rlm@470	1028 perspective and then projecting it back onto a surface in the 3D
rlm@470	1029 world.
rlm@470	1030
rlm@470	1031 #+caption: jMonkeyEngine supports multiple views to enable
rlm@470	1032 #+caption: split-screen games, like GoldenEye, which was one of
rlm@470	1033 #+caption: the first games to use split-screen views.
rlm@518	1034 #+name: goldeneye
rlm@470	1035 #+ATTR_LaTeX: :width 10cm
rlm@470	1036 [[./images/goldeneye-4-player.png]]
rlm@470	1037
rlm@470	1038 *** A Brief Description of jMonkeyEngine's Rendering Pipeline
rlm@470	1039
rlm@470	1040 jMonkeyEngine allows you to create a =ViewPort=, which represents a
rlm@470	1041 view of the simulated world. You can create as many of these as you
rlm@470	1042 want. Every frame, the =RenderManager= iterates through each
rlm@470	1043 =ViewPort=, rendering the scene in the GPU. For each =ViewPort= there
rlm@470	1044 is a =FrameBuffer= which represents the rendered image in the GPU.
rlm@470	1045
rlm@470	1046 #+caption: =ViewPorts= are cameras in the world. During each frame,
rlm@470	1047 #+caption: the =RenderManager= records a snapshot of what each view
rlm@470	1048 #+caption: is currently seeing; these snapshots are =FrameBuffer= objects.
rlm@508	1049 #+name: rendermanagers
rlm@470	1050 #+ATTR_LaTeX: :width 10cm
rlm@508	1051 [[./images/diagram_rendermanager2.png]]
rlm@470	1052
rlm@470	1053 Each =ViewPort= can have any number of attached =SceneProcessor=
rlm@470	1054 objects, which are called every time a new frame is rendered. A
rlm@470	1055 =SceneProcessor= receives its =ViewPort's= =FrameBuffer= and can do
rlm@470	1056 whatever it wants to the data. Often this consists of invoking GPU
rlm@470	1057 specific operations on the rendered image. The =SceneProcessor= can
rlm@470	1058 also copy the GPU image data to RAM and process it with the CPU.
rlm@470	1059
rlm@470	1060 *** Appropriating Views for Vision
rlm@470	1061
rlm@470	1062 Each eye in the simulated creature needs its own =ViewPort= so
rlm@470	1063 that it can see the world from its own perspective. To this
rlm@470	1064 =ViewPort=, I add a =SceneProcessor= that feeds the visual data to
rlm@470	1065 any arbitrary continuation function for further processing. That
rlm@470	1066 continuation function may perform both CPU and GPU operations on
rlm@470	1067 the data. To make this easy for the continuation function, the
rlm@470	1068 =SceneProcessor= maintains appropriately sized buffers in RAM to
rlm@470	1069 hold the data. It does not do any copying from the GPU to the CPU
rlm@470	1070 itself because it is a slow operation.
rlm@470	1071
rlm@517	1072 #+caption: Function to make the rendered scene in jMonkeyEngine
rlm@470	1073 #+caption: available for further processing.
rlm@470	1074 #+name: pipeline-1
rlm@470	1075 #+begin_listing clojure
rlm@470	1076 #+begin_src clojure
rlm@470	1077 (defn vision-pipeline
rlm@470	1078 "Create a SceneProcessor object which wraps a vision processing
rlm@470	1079 continuation function. The continuation is a function that takes
rlm@470	1080 [#^Renderer r #^FrameBuffer fb #^ByteBuffer b #^BufferedImage bi],
rlm@470	1081 each of which has already been appropriately sized."
rlm@470	1082 [continuation]
rlm@470	1083 (let [byte-buffer (atom nil)
rlm@470	1084 renderer (atom nil)
rlm@470	1085 image (atom nil)]
rlm@470	1086 (proxy [SceneProcessor] []
rlm@470	1087 (initialize
rlm@470	1088 [renderManager viewPort]
rlm@470	1089 (let [cam (.getCamera viewPort)
rlm@470	1090 width (.getWidth cam)
rlm@470	1091 height (.getHeight cam)]
rlm@470	1092 (reset! renderer (.getRenderer renderManager))
rlm@470	1093 (reset! byte-buffer
rlm@470	1094 (BufferUtils/createByteBuffer
rlm@470	1095 (* width height 4)))
rlm@470	1096 (reset! image (BufferedImage.
rlm@470	1097 width height
rlm@470	1098 BufferedImage/TYPE_4BYTE_ABGR))))
rlm@470	1099 (isInitialized [] (not (nil? @byte-buffer)))
rlm@470	1100 (reshape [_ _ _])
rlm@470	1101 (preFrame [_])
rlm@470	1102 (postQueue [_])
rlm@470	1103 (postFrame
rlm@470	1104 [#^FrameBuffer fb]
rlm@470	1105 (.clear @byte-buffer)
rlm@470	1106 (continuation @renderer fb @byte-buffer @image))
rlm@470	1107 (cleanup []))))
rlm@470	1108 #+end_src
rlm@470	1109 #+end_listing
rlm@470	1110
rlm@470	1111 The continuation function given to =vision-pipeline= above will be
rlm@470	1112 given a =Renderer= and three containers for image data. The
rlm@470	1113 =FrameBuffer= references the GPU image data, but the pixel data
rlm@470	1114 can not be used directly on the CPU. The =ByteBuffer= and
rlm@470	1115 =BufferedImage= are initially "empty" but are sized to hold the
rlm@470	1116 data in the =FrameBuffer=. I call transferring the GPU image data
rlm@470	1117 to the CPU structures "mixing" the image data.
rlm@470	1118
rlm@470	1119 *** Optical sensor arrays are described with images and referenced with metadata
rlm@470	1120
rlm@470	1121 The vision pipeline described above handles the flow of rendered
rlm@470	1122 images. Now, =CORTEX= needs simulated eyes to serve as the source
rlm@470	1123 of these images.
rlm@470	1124
rlm@470	1125 An eye is described in blender in the same way as a joint. They
rlm@470	1126 are zero dimensional empty objects with no geometry whose local
rlm@470	1127 coordinate system determines the orientation of the resulting eye.
rlm@470	1128 All eyes are children of a parent node named "eyes" just as all
rlm@470	1129 joints have a parent named "joints". An eye binds to the nearest
rlm@470	1130 physical object with =bind-sense=.
rlm@470	1131
rlm@470	1132 #+caption: Here, the camera is created based on metadata on the
rlm@470	1133 #+caption: eye-node and attached to the nearest physical object
rlm@470	1134 #+caption: with =bind-sense=
rlm@470	1135 #+name: add-eye
rlm@470	1136 #+begin_listing clojure
rlm@545	1137 #+begin_src clojure
rlm@470	1138 (defn add-eye!
rlm@470	1139 "Create a Camera centered on the current position of 'eye which
rlm@470	1140 follows the closest physical node in 'creature. The camera will
rlm@470	1141 point in the X direction and use the Z vector as up as determined
rlm@470	1142 by the rotation of these vectors in blender coordinate space. Use
rlm@470	1143 XZY rotation for the node in blender."
rlm@470	1144 [#^Node creature #^Spatial eye]
rlm@470	1145 (let [target (closest-node creature eye)
rlm@470	1146 [cam-width cam-height]
rlm@470	1147 ;;[640 480] ;; graphics card on laptop doesn't support
rlm@517	1148 ;; arbitrary dimensions.
rlm@470	1149 (eye-dimensions eye)
rlm@470	1150 cam (Camera. cam-width cam-height)
rlm@470	1151 rot (.getWorldRotation eye)]
rlm@470	1152 (.setLocation cam (.getWorldTranslation eye))
rlm@470	1153 (.lookAtDirection
rlm@470	1154 cam ; this part is not a mistake and
rlm@470	1155 (.mult rot Vector3f/UNIT_X) ; is consistent with using Z in
rlm@470	1156 (.mult rot Vector3f/UNIT_Y)) ; blender as the UP vector.
rlm@470	1157 (.setFrustumPerspective
rlm@470	1158 cam (float 45)
rlm@470	1159 (float (/ (.getWidth cam) (.getHeight cam)))
rlm@470	1160 (float 1)
rlm@470	1161 (float 1000))
rlm@470	1162 (bind-sense target cam) cam))
rlm@545	1163 #+end_src
rlm@470	1164 #+end_listing
rlm@470	1165
rlm@470	1166 *** Simulated Retina
rlm@470	1167
rlm@470	1168 An eye is a surface (the retina) which contains many discrete
rlm@470	1169 sensors to detect light. These sensors can have different
rlm@470	1170 light-sensing properties. In humans, each discrete sensor is
rlm@470	1171 sensitive to red, blue, green, or gray. These different types of
rlm@470	1172 sensors can have different spatial distributions along the retina.
rlm@470	1173 In humans, there is a fovea in the center of the retina which has
rlm@470	1174 a very high density of color sensors, and a blind spot which has
rlm@470	1175 no sensors at all. Sensor density decreases in proportion to
rlm@470	1176 distance from the fovea.
rlm@470	1177
rlm@470	1178 I want to be able to model any retinal configuration, so my
rlm@470	1179 eye-nodes in blender contain metadata pointing to images that
rlm@470	1180 describe the precise position of the individual sensors using
rlm@470	1181 white pixels. The meta-data also describes the precise sensitivity
rlm@470	1182 to light that the sensors described in the image have. An eye can
rlm@470	1183 contain any number of these images. For example, the metadata for
rlm@470	1184 an eye might look like this:
rlm@470	1185
rlm@470	1186 #+begin_src clojure
rlm@470	1187 {0xFF0000 "Models/test-creature/retina-small.png"}
rlm@470	1188 #+end_src
rlm@470	1189
rlm@470	1190 #+caption: An example retinal profile image. White pixels are
rlm@470	1191 #+caption: photo-sensitive elements. The distribution of white
rlm@470	1192 #+caption: pixels is denser in the middle and falls off at the
rlm@470	1193 #+caption: edges and is inspired by the human retina.
rlm@470	1194 #+name: retina
rlm@510	1195 #+ATTR_LaTeX: :width 7cm
rlm@470	1196 [[./images/retina-small.png]]
rlm@470	1197
rlm@545	1198 Together, the number 0xFF0000 and the image above describe the
rlm@545	1199 placement of red-sensitive sensory elements.
rlm@470	1200
rlm@470	1201 Meta-data to very crudely approximate a human eye might be
rlm@470	1202 something like this:
rlm@470	1203
rlm@470	1204 #+begin_src clojure
rlm@470	1205 (let [retinal-profile "Models/test-creature/retina-small.png"]
rlm@470	1206 {0xFF0000 retinal-profile
rlm@470	1207 0x00FF00 retinal-profile
rlm@470	1208 0x0000FF retinal-profile
rlm@470	1209 0xFFFFFF retinal-profile})
rlm@470	1210 #+end_src
rlm@470	1211
rlm@470	1212 The numbers that serve as keys in the map determine a sensor's
rlm@470	1213 relative sensitivity to the channels red, green, and blue. These
rlm@470	1214 sensitivity values are packed into an integer in the order
rlm@470	1215 =\|_\|R\|G\|B\|= in 8-bit fields. The RGB values of a pixel in the
rlm@470	1216 image are added together with these sensitivities as linear
rlm@470	1217 weights. Therefore, 0xFF0000 means sensitive to red only while
rlm@470	1218 0xFFFFFF means sensitive to all colors equally (gray).
rlm@470	1219
rlm@470	1220 #+caption: This is the core of vision in =CORTEX=. A given eye node
rlm@470	1221 #+caption: is converted into a function that returns visual
rlm@470	1222 #+caption: information from the simulation.
rlm@471	1223 #+name: vision-kernel
rlm@470	1224 #+begin_listing clojure
rlm@508	1225 #+BEGIN_SRC clojure
rlm@470	1226 (defn vision-kernel
rlm@470	1227 "Returns a list of functions, each of which will return a color
rlm@470	1228 channel's worth of visual information when called inside a running
rlm@470	1229 simulation."
rlm@470	1230 [#^Node creature #^Spatial eye & {skip :skip :or {skip 0}}]
rlm@470	1231 (let [retinal-map (retina-sensor-profile eye)
rlm@470	1232 camera (add-eye! creature eye)
rlm@470	1233 vision-image
rlm@470	1234 (atom
rlm@470	1235 (BufferedImage. (.getWidth camera)
rlm@470	1236 (.getHeight camera)
rlm@470	1237 BufferedImage/TYPE_BYTE_BINARY))
rlm@470	1238 register-eye!
rlm@470	1239 (runonce
rlm@470	1240 (fn [world]
rlm@470	1241 (add-camera!
rlm@470	1242 world camera
rlm@470	1243 (let [counter (atom 0)]
rlm@470	1244 (fn [r fb bb bi]
rlm@470	1245 (if (zero? (rem (swap! counter inc) (inc skip)))
rlm@470	1246 (reset! vision-image
rlm@470	1247 (BufferedImage! r fb bb bi))))))))]
rlm@470	1248 (vec
rlm@470	1249 (map
rlm@470	1250 (fn [[key image]]
rlm@470	1251 (let [whites (white-coordinates image)
rlm@470	1252 topology (vec (collapse whites))
rlm@470	1253 sensitivity (sensitivity-presets key key)]
rlm@470	1254 (attached-viewport.
rlm@470	1255 (fn [world]
rlm@470	1256 (register-eye! world)
rlm@470	1257 (vector
rlm@470	1258 topology
rlm@470	1259 (vec
rlm@470	1260 (for [[x y] whites]
rlm@470	1261 (pixel-sense
rlm@470	1262 sensitivity
rlm@470	1263 (.getRGB @vision-image x y))))))
rlm@470	1264 register-eye!)))
rlm@470	1265 retinal-map))))
rlm@508	1266 #+END_SRC
rlm@470	1267 #+end_listing
rlm@470	1268
rlm@470	1269 Note that since each of the functions generated by =vision-kernel=
rlm@470	1270 shares the same =register-eye!= function, the eye will be
rlm@470	1271 registered only once the first time any of the functions from the
rlm@470	1272 list returned by =vision-kernel= is called. Each of the functions
rlm@470	1273 returned by =vision-kernel= also allows access to the =Viewport=
rlm@470	1274 through which it receives images.
rlm@470	1275
rlm@470	1276 All the hard work has been done; all that remains is to apply
rlm@470	1277 =vision-kernel= to each eye in the creature and gather the results
rlm@470	1278 into one list of functions.
rlm@470	1279
rlm@470	1280
rlm@470	1281 #+caption: With =vision!=, =CORTEX= is already a fine simulation
rlm@470	1282 #+caption: environment for experimenting with different types of
rlm@470	1283 #+caption: eyes.
rlm@470	1284 #+name: vision!
rlm@470	1285 #+begin_listing clojure
rlm@508	1286 #+BEGIN_SRC clojure
rlm@470	1287 (defn vision!
rlm@470	1288 "Returns a list of functions, each of which returns visual sensory
rlm@470	1289 data when called inside a running simulation."
rlm@470	1290 [#^Node creature & {skip :skip :or {skip 0}}]
rlm@470	1291 (reduce
rlm@470	1292 concat
rlm@470	1293 (for [eye (eyes creature)]
rlm@470	1294 (vision-kernel creature eye))))
rlm@508	1295 #+END_SRC
rlm@470	1296 #+end_listing
rlm@470	1297
rlm@471	1298 #+caption: Simulated vision with a test creature and the
rlm@471	1299 #+caption: human-like eye approximation. Notice how each channel
rlm@471	1300 #+caption: of the eye responds differently to the differently
rlm@471	1301 #+caption: colored balls.
rlm@471	1302 #+name: worm-vision-test.
rlm@471	1303 #+ATTR_LaTeX: :width 13cm
rlm@471	1304 [[./images/worm-vision.png]]
rlm@470	1305
rlm@471	1306 The vision code is not much more complicated than the body code,
rlm@471	1307 and enables multiple further paths for simulated vision. For
rlm@471	1308 example, it is quite easy to create bifocal vision -- you just
rlm@471	1309 make two eyes next to each other in blender! It is also possible
rlm@471	1310 to encode vision transforms in the retinal files. For example, the
rlm@471	1311 human like retina file in figure \ref{retina} approximates a
rlm@471	1312 log-polar transform.
rlm@470	1313
rlm@471	1314 This vision code has already been absorbed by the jMonkeyEngine
rlm@471	1315 community and is now (in modified form) part of a system for
rlm@471	1316 capturing in-game video to a file.
rlm@470	1317
rlm@511	1318 ** ...but hearing must be built from scratch
rlm@514	1319
rlm@472	1320 At the end of this section I will have simulated ears that work the
rlm@472	1321 same way as the simulated eyes in the last section. I will be able to
rlm@472	1322 place any number of ear-nodes in a blender file, and they will bind to
rlm@472	1323 the closest physical object and follow it as it moves around. Each ear
rlm@472	1324 will provide access to the sound data it picks up between every frame.
rlm@472	1325
rlm@472	1326 Hearing is one of the more difficult senses to simulate, because there
rlm@472	1327 is less support for obtaining the actual sound data that is processed
rlm@472	1328 by jMonkeyEngine3. There is no "split-screen" support for rendering
rlm@472	1329 sound from different points of view, and there is no way to directly
rlm@472	1330 access the rendered sound data.
rlm@472	1331
rlm@472	1332 =CORTEX='s hearing is unique because it does not have any
rlm@472	1333 limitations compared to other simulation environments. As far as I
rlm@517	1334 know, there is no other system that supports multiple listeners,
rlm@472	1335 and the sound demo at the end of this section is the first time
rlm@472	1336 it's been done in a video game environment.
rlm@472	1337
rlm@472	1338 *** Brief Description of jMonkeyEngine's Sound System
rlm@472	1339
rlm@472	1340 jMonkeyEngine's sound system works as follows:
rlm@472	1341
rlm@472	1342 - jMonkeyEngine uses the =AppSettings= for the particular
rlm@472	1343 application to determine what sort of =AudioRenderer= should be
rlm@472	1344 used.
rlm@472	1345 - Although some support is provided for multiple AudioRendering
rlm@472	1346 backends, jMonkeyEngine at the time of this writing will either
rlm@472	1347 pick no =AudioRenderer= at all, or the =LwjglAudioRenderer=.
rlm@472	1348 - jMonkeyEngine tries to figure out what sort of system you're
rlm@472	1349 running and extracts the appropriate native libraries.
rlm@472	1350 - The =LwjglAudioRenderer= uses the [[http://lwjgl.org/][=LWJGL=]] (LightWeight Java Game
rlm@472	1351 Library) bindings to interface with a C library called [[http://kcat.strangesoft.net/openal.html][=OpenAL=]]
rlm@472	1352 - =OpenAL= renders the 3D sound and feeds the rendered sound
rlm@472	1353 directly to any of various sound output devices with which it
rlm@472	1354 knows how to communicate.
rlm@472	1355
rlm@472	1356 A consequence of this is that there's no way to access the actual
rlm@472	1357 sound data produced by =OpenAL=. Even worse, =OpenAL= only supports
rlm@472	1358 one /listener/ (it renders sound data from only one perspective),
rlm@472	1359 which normally isn't a problem for games, but becomes a problem
rlm@472	1360 when trying to make multiple AI creatures that can each hear the
rlm@472	1361 world from a different perspective.
rlm@472	1362
rlm@472	1363 To make many AI creatures in jMonkeyEngine that can each hear the
rlm@472	1364 world from their own perspective, or to make a single creature with
rlm@472	1365 many ears, it is necessary to go all the way back to =OpenAL= and
rlm@472	1366 implement support for simulated hearing there.
rlm@472	1367
rlm@472	1368 *** Extending =OpenAl=
rlm@472	1369
rlm@472	1370 Extending =OpenAL= to support multiple listeners requires 500
rlm@472	1371 lines of =C= code and is too hairy to mention here. Instead, I
rlm@472	1372 will show a small amount of extension code and go over the high
rlm@517	1373 level strategy. Full source is of course available with the
rlm@472	1374 =CORTEX= distribution if you're interested.
rlm@472	1375
rlm@472	1376 =OpenAL= goes to great lengths to support many different systems,
rlm@472	1377 all with different sound capabilities and interfaces. It
rlm@472	1378 accomplishes this difficult task by providing code for many
rlm@472	1379 different sound backends in pseudo-objects called /Devices/.
rlm@472	1380 There's a device for the Linux Open Sound System and the Advanced
rlm@472	1381 Linux Sound Architecture, there's one for Direct Sound on Windows,
rlm@472	1382 and there's even one for Solaris. =OpenAL= solves the problem of
rlm@472	1383 platform independence by providing all these Devices.
rlm@472	1384
rlm@472	1385 Wrapper libraries such as LWJGL are free to examine the system on
rlm@472	1386 which they are running and then select an appropriate device for
rlm@472	1387 that system.
rlm@472	1388
rlm@472	1389 There are also a few "special" devices that don't interface with
rlm@472	1390 any particular system. These include the Null Device, which
rlm@472	1391 doesn't do anything, and the Wave Device, which writes whatever
rlm@472	1392 sound it receives to a file, if everything has been set up
rlm@472	1393 correctly when configuring =OpenAL=.
rlm@472	1394
rlm@517	1395 Actual mixing (Doppler shift and distance.environment-based
rlm@472	1396 attenuation) of the sound data happens in the Devices, and they
rlm@472	1397 are the only point in the sound rendering process where this data
rlm@472	1398 is available.
rlm@472	1399
rlm@472	1400 Therefore, in order to support multiple listeners, and get the
rlm@472	1401 sound data in a form that the AIs can use, it is necessary to
rlm@472	1402 create a new Device which supports this feature.
rlm@472	1403
rlm@472	1404 Adding a device to OpenAL is rather tricky -- there are five
rlm@472	1405 separate files in the =OpenAL= source tree that must be modified
rlm@472	1406 to do so. I named my device the "Multiple Audio Send" Device, or
rlm@472	1407 =Send= Device for short, since it sends audio data back to the
rlm@472	1408 calling application like an Aux-Send cable on a mixing board.
rlm@472	1409
rlm@472	1410 The main idea behind the Send device is to take advantage of the
rlm@472	1411 fact that LWJGL only manages one /context/ when using OpenAL. A
rlm@472	1412 /context/ is like a container that holds samples and keeps track
rlm@472	1413 of where the listener is. In order to support multiple listeners,
rlm@472	1414 the Send device identifies the LWJGL context as the master
rlm@472	1415 context, and creates any number of slave contexts to represent
rlm@472	1416 additional listeners. Every time the device renders sound, it
rlm@472	1417 synchronizes every source from the master LWJGL context to the
rlm@472	1418 slave contexts. Then, it renders each context separately, using a
rlm@472	1419 different listener for each one. The rendered sound is made
rlm@472	1420 available via JNI to jMonkeyEngine.
rlm@472	1421
rlm@472	1422 Switching between contexts is not the normal operation of a
rlm@472	1423 Device, and one of the problems with doing so is that a Device
rlm@472	1424 normally keeps around a few pieces of state such as the
rlm@472	1425 =ClickRemoval= array above which will become corrupted if the
rlm@472	1426 contexts are not rendered in parallel. The solution is to create a
rlm@472	1427 copy of this normally global device state for each context, and
rlm@472	1428 copy it back and forth into and out of the actual device state
rlm@472	1429 whenever a context is rendered.
rlm@472	1430
rlm@472	1431 The core of the =Send= device is the =syncSources= function, which
rlm@472	1432 does the job of copying all relevant data from one context to
rlm@472	1433 another.
rlm@472	1434
rlm@472	1435 #+caption: Program for extending =OpenAL= to support multiple
rlm@472	1436 #+caption: listeners via context copying/switching.
rlm@472	1437 #+name: sync-openal-sources
rlm@509	1438 #+begin_listing c
rlm@509	1439 #+BEGIN_SRC c
rlm@472	1440 void syncSources(ALsource masterSource, ALsource slaveSource,
rlm@472	1441 ALCcontext masterCtx, ALCcontext slaveCtx){
rlm@472	1442 ALuint master = masterSource->source;
rlm@472	1443 ALuint slave = slaveSource->source;
rlm@472	1444 ALCcontext *current = alcGetCurrentContext();
rlm@472	1445
rlm@472	1446 syncSourcef(master,slave,masterCtx,slaveCtx,AL_PITCH);
rlm@472	1447 syncSourcef(master,slave,masterCtx,slaveCtx,AL_GAIN);
rlm@472	1448 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_DISTANCE);
rlm@472	1449 syncSourcef(master,slave,masterCtx,slaveCtx,AL_ROLLOFF_FACTOR);
rlm@472	1450 syncSourcef(master,slave,masterCtx,slaveCtx,AL_REFERENCE_DISTANCE);
rlm@472	1451 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MIN_GAIN);
rlm@472	1452 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_GAIN);
rlm@472	1453 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_GAIN);
rlm@472	1454 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_INNER_ANGLE);
rlm@472	1455 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_ANGLE);
rlm@472	1456 syncSourcef(master,slave,masterCtx,slaveCtx,AL_SEC_OFFSET);
rlm@472	1457 syncSourcef(master,slave,masterCtx,slaveCtx,AL_SAMPLE_OFFSET);
rlm@472	1458 syncSourcef(master,slave,masterCtx,slaveCtx,AL_BYTE_OFFSET);
rlm@472	1459
rlm@472	1460 syncSource3f(master,slave,masterCtx,slaveCtx,AL_POSITION);
rlm@472	1461 syncSource3f(master,slave,masterCtx,slaveCtx,AL_VELOCITY);
rlm@472	1462 syncSource3f(master,slave,masterCtx,slaveCtx,AL_DIRECTION);
rlm@472	1463
rlm@472	1464 syncSourcei(master,slave,masterCtx,slaveCtx,AL_SOURCE_RELATIVE);
rlm@472	1465 syncSourcei(master,slave,masterCtx,slaveCtx,AL_LOOPING);
rlm@472	1466
rlm@472	1467 alcMakeContextCurrent(masterCtx);
rlm@472	1468 ALint source_type;
rlm@472	1469 alGetSourcei(master, AL_SOURCE_TYPE, &source_type);
rlm@472	1470
rlm@472	1471 // Only static sources are currently synchronized!
rlm@472	1472 if (AL_STATIC == source_type){
rlm@472	1473 ALint master_buffer;
rlm@472	1474 ALint slave_buffer;
rlm@472	1475 alGetSourcei(master, AL_BUFFER, &master_buffer);
rlm@472	1476 alcMakeContextCurrent(slaveCtx);
rlm@472	1477 alGetSourcei(slave, AL_BUFFER, &slave_buffer);
rlm@472	1478 if (master_buffer != slave_buffer){
rlm@472	1479 alSourcei(slave, AL_BUFFER, master_buffer);
rlm@472	1480 }
rlm@472	1481 }
rlm@472	1482
rlm@472	1483 // Synchronize the state of the two sources.
rlm@472	1484 alcMakeContextCurrent(masterCtx);
rlm@472	1485 ALint masterState;
rlm@472	1486 ALint slaveState;
rlm@472	1487
rlm@472	1488 alGetSourcei(master, AL_SOURCE_STATE, &masterState);
rlm@472	1489 alcMakeContextCurrent(slaveCtx);
rlm@472	1490 alGetSourcei(slave, AL_SOURCE_STATE, &slaveState);
rlm@472	1491
rlm@472	1492 if (masterState != slaveState){
rlm@472	1493 switch (masterState){
rlm@472	1494 case AL_INITIAL : alSourceRewind(slave); break;
rlm@472	1495 case AL_PLAYING : alSourcePlay(slave); break;
rlm@472	1496 case AL_PAUSED : alSourcePause(slave); break;
rlm@472	1497 case AL_STOPPED : alSourceStop(slave); break;
rlm@472	1498 }
rlm@472	1499 }
rlm@472	1500 // Restore whatever context was previously active.
rlm@472	1501 alcMakeContextCurrent(current);
rlm@472	1502 }
rlm@508	1503 #+END_SRC
rlm@472	1504 #+end_listing
rlm@472	1505
rlm@472	1506 With this special context-switching device, and some ugly JNI
rlm@472	1507 bindings that are not worth mentioning, =CORTEX= gains the ability
rlm@472	1508 to access multiple sound streams from =OpenAL=.
rlm@472	1509
rlm@472	1510 #+caption: Program to create an ear from a blender empty node. The ear
rlm@472	1511 #+caption: follows around the nearest physical object and passes
rlm@472	1512 #+caption: all sensory data to a continuation function.
rlm@472	1513 #+name: add-ear
rlm@472	1514 #+begin_listing clojure
rlm@508	1515 #+BEGIN_SRC clojure
rlm@472	1516 (defn add-ear!
rlm@472	1517 "Create a Listener centered on the current position of 'ear
rlm@472	1518 which follows the closest physical node in 'creature and
rlm@472	1519 sends sound data to 'continuation."
rlm@472	1520 [#^Application world #^Node creature #^Spatial ear continuation]
rlm@472	1521 (let [target (closest-node creature ear)
rlm@472	1522 lis (Listener.)
rlm@472	1523 audio-renderer (.getAudioRenderer world)
rlm@472	1524 sp (hearing-pipeline continuation)]
rlm@472	1525 (.setLocation lis (.getWorldTranslation ear))
rlm@472	1526 (.setRotation lis (.getWorldRotation ear))
rlm@472	1527 (bind-sense target lis)
rlm@472	1528 (update-listener-velocity! target lis)
rlm@472	1529 (.addListener audio-renderer lis)
rlm@472	1530 (.registerSoundProcessor audio-renderer lis sp)))
rlm@508	1531 #+END_SRC
rlm@472	1532 #+end_listing
rlm@472	1533
rlm@472	1534 The =Send= device, unlike most of the other devices in =OpenAL=,
rlm@472	1535 does not render sound unless asked. This enables the system to
rlm@472	1536 slow down or speed up depending on the needs of the AIs who are
rlm@472	1537 using it to listen. If the device tried to render samples in
rlm@472	1538 real-time, a complicated AI whose mind takes 100 seconds of
rlm@472	1539 computer time to simulate 1 second of AI-time would miss almost
rlm@472	1540 all of the sound in its environment!
rlm@472	1541
rlm@472	1542 #+caption: Program to enable arbitrary hearing in =CORTEX=
rlm@472	1543 #+name: hearing
rlm@472	1544 #+begin_listing clojure
rlm@508	1545 #+BEGIN_SRC clojure
rlm@472	1546 (defn hearing-kernel
rlm@472	1547 "Returns a function which returns auditory sensory data when called
rlm@472	1548 inside a running simulation."
rlm@472	1549 [#^Node creature #^Spatial ear]
rlm@472	1550 (let [hearing-data (atom [])
rlm@472	1551 register-listener!
rlm@472	1552 (runonce
rlm@472	1553 (fn [#^Application world]
rlm@472	1554 (add-ear!
rlm@472	1555 world creature ear
rlm@472	1556 (comp #(reset! hearing-data %)
rlm@472	1557 byteBuffer->pulse-vector))))]
rlm@472	1558 (fn [#^Application world]
rlm@472	1559 (register-listener! world)
rlm@472	1560 (let [data @hearing-data
rlm@472	1561 topology
rlm@472	1562 (vec (map #(vector % 0) (range 0 (count data))))]
rlm@472	1563 [topology data]))))
rlm@472	1564
rlm@472	1565 (defn hearing!
rlm@472	1566 "Endow the creature in a particular world with the sense of
rlm@472	1567 hearing. Will return a sequence of functions, one for each ear,
rlm@472	1568 which when called will return the auditory data from that ear."
rlm@472	1569 [#^Node creature]
rlm@472	1570 (for [ear (ears creature)]
rlm@472	1571 (hearing-kernel creature ear)))
rlm@508	1572 #+END_SRC
rlm@472	1573 #+end_listing
rlm@472	1574
rlm@472	1575 Armed with these functions, =CORTEX= is able to test possibly the
rlm@472	1576 first ever instance of multiple listeners in a video game engine
rlm@472	1577 based simulation!
rlm@472	1578
rlm@472	1579 #+caption: Here a simple creature responds to sound by changing
rlm@472	1580 #+caption: its color from gray to green when the total volume
rlm@472	1581 #+caption: goes over a threshold.
rlm@472	1582 #+name: sound-test
rlm@472	1583 #+begin_listing java
rlm@508	1584 #+BEGIN_SRC java
rlm@472	1585 /**
rlm@472	1586 * Respond to sound! This is the brain of an AI entity that
rlm@472	1587 * hears its surroundings and reacts to them.
rlm@472	1588 */
rlm@472	1589 public void process(ByteBuffer audioSamples,
rlm@472	1590 int numSamples, AudioFormat format) {
rlm@472	1591 audioSamples.clear();
rlm@472	1592 byte[] data = new byte[numSamples];
rlm@472	1593 float[] out = new float[numSamples];
rlm@472	1594 audioSamples.get(data);
rlm@472	1595 FloatSampleTools.
rlm@472	1596 byte2floatInterleaved
rlm@472	1597 (data, 0, out, 0, numSamples/format.getFrameSize(), format);
rlm@472	1598
rlm@472	1599 float max = Float.NEGATIVE_INFINITY;
rlm@472	1600 for (float f : out){if (f > max) max = f;}
rlm@472	1601 audioSamples.clear();
rlm@472	1602
rlm@472	1603 if (max > 0.1){
rlm@472	1604 entity.getMaterial().setColor("Color", ColorRGBA.Green);
rlm@472	1605 }
rlm@472	1606 else {
rlm@472	1607 entity.getMaterial().setColor("Color", ColorRGBA.Gray);
rlm@472	1608 }
rlm@508	1609 #+END_SRC
rlm@472	1610 #+end_listing
rlm@472	1611
rlm@517	1612 #+caption: First ever simulation of multiple listeners in =CORTEX=.
rlm@472	1613 #+caption: Each cube is a creature which processes sound data with
rlm@472	1614 #+caption: the =process= function from listing \ref{sound-test}.
rlm@517	1615 #+caption: the ball is constantly emitting a pure tone of
rlm@472	1616 #+caption: constant volume. As it approaches the cubes, they each
rlm@472	1617 #+caption: change color in response to the sound.
rlm@472	1618 #+name: sound-cubes.
rlm@472	1619 #+ATTR_LaTeX: :width 10cm
rlm@509	1620 [[./images/java-hearing-test.png]]
rlm@472	1621
rlm@472	1622 This system of hearing has also been co-opted by the
rlm@472	1623 jMonkeyEngine3 community and is used to record audio for demo
rlm@472	1624 videos.
rlm@472	1625
rlm@511	1626 ** Hundreds of hair-like elements provide a sense of touch
rlm@436	1627
rlm@474	1628 Touch is critical to navigation and spatial reasoning and as such I
rlm@474	1629 need a simulated version of it to give to my AI creatures.
rlm@474	1630
rlm@474	1631 Human skin has a wide array of touch sensors, each of which
rlm@474	1632 specialize in detecting different vibrational modes and pressures.
rlm@474	1633 These sensors can integrate a vast expanse of skin (i.e. your
rlm@474	1634 entire palm), or a tiny patch of skin at the tip of your finger.
rlm@474	1635 The hairs of the skin help detect objects before they even come
rlm@474	1636 into contact with the skin proper.
rlm@474	1637
rlm@474	1638 However, touch in my simulated world can not exactly correspond to
rlm@474	1639 human touch because my creatures are made out of completely rigid
rlm@474	1640 segments that don't deform like human skin.
rlm@474	1641
rlm@474	1642 Instead of measuring deformation or vibration, I surround each
rlm@474	1643 rigid part with a plenitude of hair-like objects (/feelers/) which
rlm@474	1644 do not interact with the physical world. Physical objects can pass
rlm@474	1645 through them with no effect. The feelers are able to tell when
rlm@474	1646 other objects pass through them, and they constantly report how
rlm@474	1647 much of their extent is covered. So even though the creature's body
rlm@474	1648 parts do not deform, the feelers create a margin around those body
rlm@474	1649 parts which achieves a sense of touch which is a hybrid between a
rlm@474	1650 human's sense of deformation and sense from hairs.
rlm@474	1651
rlm@474	1652 Implementing touch in jMonkeyEngine follows a different technical
rlm@474	1653 route than vision and hearing. Those two senses piggybacked off
rlm@474	1654 jMonkeyEngine's 3D audio and video rendering subsystems. To
rlm@474	1655 simulate touch, I use jMonkeyEngine's physics system to execute
rlm@474	1656 many small collision detections, one for each feeler. The placement
rlm@474	1657 of the feelers is determined by a UV-mapped image which shows where
rlm@474	1658 each feeler should be on the 3D surface of the body.
rlm@474	1659
rlm@477	1660 *** Defining Touch Meta-Data in Blender
rlm@474	1661
rlm@474	1662 Each geometry can have a single UV map which describes the
rlm@474	1663 position of the feelers which will constitute its sense of touch.
rlm@474	1664 This image path is stored under the ``touch'' key. The image itself
rlm@474	1665 is black and white, with black meaning a feeler length of 0 (no
rlm@474	1666 feeler is present) and white meaning a feeler length of =scale=,
rlm@474	1667 which is a float stored under the key "scale".
rlm@474	1668
rlm@475	1669 #+caption: Touch does not use empty nodes, to store metadata,
rlm@475	1670 #+caption: because the metadata of each solid part of a
rlm@475	1671 #+caption: creature's body is sufficient.
rlm@475	1672 #+name: touch-meta-data
rlm@475	1673 #+begin_listing clojure
rlm@477	1674 #+BEGIN_SRC clojure
rlm@474	1675 (defn tactile-sensor-profile
rlm@474	1676 "Return the touch-sensor distribution image in BufferedImage format,
rlm@474	1677 or nil if it does not exist."
rlm@474	1678 [#^Geometry obj]
rlm@474	1679 (if-let [image-path (meta-data obj "touch")]
rlm@474	1680 (load-image image-path)))
rlm@474	1681
rlm@474	1682 (defn tactile-scale
rlm@474	1683 "Return the length of each feeler. Default scale is 0.01
rlm@474	1684 jMonkeyEngine units."
rlm@474	1685 [#^Geometry obj]
rlm@474	1686 (if-let [scale (meta-data obj "scale")]
rlm@474	1687 scale 0.1))
rlm@477	1688 #+END_SRC
rlm@475	1689 #+end_listing
rlm@474	1690
rlm@475	1691 Here is an example of a UV-map which specifies the position of
rlm@475	1692 touch sensors along the surface of the upper segment of a fingertip.
rlm@474	1693
rlm@475	1694 #+caption: This is the tactile-sensor-profile for the upper segment
rlm@475	1695 #+caption: of a fingertip. It defines regions of high touch sensitivity
rlm@475	1696 #+caption: (where there are many white pixels) and regions of low
rlm@475	1697 #+caption: sensitivity (where white pixels are sparse).
rlm@486	1698 #+name: fingertip-UV
rlm@477	1699 #+ATTR_LaTeX: :width 13cm
rlm@477	1700 [[./images/finger-UV.png]]
rlm@474	1701
rlm@477	1702 *** Implementation Summary
rlm@474	1703
rlm@474	1704 To simulate touch there are three conceptual steps. For each solid
rlm@474	1705 object in the creature, you first have to get UV image and scale
rlm@474	1706 parameter which define the position and length of the feelers.
rlm@474	1707 Then, you use the triangles which comprise the mesh and the UV
rlm@474	1708 data stored in the mesh to determine the world-space position and
rlm@474	1709 orientation of each feeler. Then once every frame, update these
rlm@474	1710 positions and orientations to match the current position and
rlm@474	1711 orientation of the object, and use physics collision detection to
rlm@474	1712 gather tactile data.
rlm@474	1713
rlm@474	1714 Extracting the meta-data has already been described. The third
rlm@474	1715 step, physics collision detection, is handled in =touch-kernel=.
rlm@474	1716 Translating the positions and orientations of the feelers from the
rlm@474	1717 UV-map to world-space is itself a three-step process.
rlm@474	1718
rlm@475	1719 - Find the triangles which make up the mesh in pixel-space and in
rlm@505	1720 world-space. \\(=triangles=, =pixel-triangles=).
rlm@474	1721
rlm@475	1722 - Find the coordinates of each feeler in world-space. These are
rlm@475	1723 the origins of the feelers. (=feeler-origins=).
rlm@474	1724
rlm@475	1725 - Calculate the normals of the triangles in world space, and add
rlm@475	1726 them to each of the origins of the feelers. These are the
rlm@475	1727 normalized coordinates of the tips of the feelers.
rlm@475	1728 (=feeler-tips=).
rlm@474	1729
rlm@477	1730 *** Triangle Math
rlm@474	1731
rlm@475	1732 The rigid objects which make up a creature have an underlying
rlm@475	1733 =Geometry=, which is a =Mesh= plus a =Material= and other
rlm@475	1734 important data involved with displaying the object.
rlm@475	1735
rlm@475	1736 A =Mesh= is composed of =Triangles=, and each =Triangle= has three
rlm@475	1737 vertices which have coordinates in world space and UV space.
rlm@475	1738
rlm@475	1739 Here, =triangles= gets all the world-space triangles which
rlm@475	1740 comprise a mesh, while =pixel-triangles= gets those same triangles
rlm@475	1741 expressed in pixel coordinates (which are UV coordinates scaled to
rlm@475	1742 fit the height and width of the UV image).
rlm@474	1743
rlm@475	1744 #+caption: Programs to extract triangles from a geometry and get
rlm@517	1745 #+caption: their vertices in both world and UV-coordinates.
rlm@475	1746 #+name: get-triangles
rlm@475	1747 #+begin_listing clojure
rlm@477	1748 #+BEGIN_SRC clojure
rlm@474	1749 (defn triangle
rlm@474	1750 "Get the triangle specified by triangle-index from the mesh."
rlm@474	1751 [#^Geometry geo triangle-index]
rlm@474	1752 (triangle-seq
rlm@474	1753 (let [scratch (Triangle.)]
rlm@474	1754 (.getTriangle (.getMesh geo) triangle-index scratch) scratch)))
rlm@474	1755
rlm@474	1756 (defn triangles
rlm@474	1757 "Return a sequence of all the Triangles which comprise a given
rlm@474	1758 Geometry."
rlm@474	1759 [#^Geometry geo]
rlm@474	1760 (map (partial triangle geo) (range (.getTriangleCount (.getMesh geo)))))
rlm@474	1761
rlm@474	1762 (defn triangle-vertex-indices
rlm@474	1763 "Get the triangle vertex indices of a given triangle from a given
rlm@474	1764 mesh."
rlm@474	1765 [#^Mesh mesh triangle-index]
rlm@474	1766 (let [indices (int-array 3)]
rlm@474	1767 (.getTriangle mesh triangle-index indices)
rlm@474	1768 (vec indices)))
rlm@474	1769
rlm@475	1770 (defn vertex-UV-coord
rlm@474	1771 "Get the UV-coordinates of the vertex named by vertex-index"
rlm@474	1772 [#^Mesh mesh vertex-index]
rlm@474	1773 (let [UV-buffer
rlm@474	1774 (.getData
rlm@474	1775 (.getBuffer
rlm@474	1776 mesh
rlm@474	1777 VertexBuffer$Type/TexCoord))]
rlm@474	1778 [(.get UV-buffer (* vertex-index 2))
rlm@474	1779 (.get UV-buffer (+ 1 (* vertex-index 2)))]))
rlm@474	1780
rlm@474	1781 (defn pixel-triangle [#^Geometry geo image index]
rlm@474	1782 (let [mesh (.getMesh geo)
rlm@474	1783 width (.getWidth image)
rlm@474	1784 height (.getHeight image)]
rlm@474	1785 (vec (map (fn [[u v]] (vector (* width u) (* height v)))
rlm@474	1786 (map (partial vertex-UV-coord mesh)
rlm@474	1787 (triangle-vertex-indices mesh index))))))
rlm@474	1788
rlm@474	1789 (defn pixel-triangles
rlm@474	1790 "The pixel-space triangles of the Geometry, in the same order as
rlm@474	1791 (triangles geo)"
rlm@474	1792 [#^Geometry geo image]
rlm@474	1793 (let [height (.getHeight image)
rlm@474	1794 width (.getWidth image)]
rlm@474	1795 (map (partial pixel-triangle geo image)
rlm@474	1796 (range (.getTriangleCount (.getMesh geo))))))
rlm@477	1797 #+END_SRC
rlm@475	1798 #+end_listing
rlm@475	1799
rlm@474	1800 *** The Affine Transform from one Triangle to Another
rlm@474	1801
rlm@475	1802 =pixel-triangles= gives us the mesh triangles expressed in pixel
rlm@475	1803 coordinates and =triangles= gives us the mesh triangles expressed
rlm@475	1804 in world coordinates. The tactile-sensor-profile gives the
rlm@475	1805 position of each feeler in pixel-space. In order to convert
rlm@475	1806 pixel-space coordinates into world-space coordinates we need
rlm@475	1807 something that takes coordinates on the surface of one triangle
rlm@475	1808 and gives the corresponding coordinates on the surface of another
rlm@475	1809 triangle.
rlm@475	1810
rlm@475	1811 Triangles are [[http://mathworld.wolfram.com/AffineTransformation.html ][affine]], which means any triangle can be transformed
rlm@475	1812 into any other by a combination of translation, scaling, and
rlm@475	1813 rotation. The affine transformation from one triangle to another
rlm@475	1814 is readily computable if the triangle is expressed in terms of a
rlm@475	1815 $4x4$ matrix.
rlm@476	1816
rlm@476	1817 #+BEGIN_LaTeX
rlm@476	1818 $$
rlm@475	1819 \begin{bmatrix}
rlm@475	1820 x_1 & x_2 & x_3 & n_x \\
rlm@475	1821 y_1 & y_2 & y_3 & n_y \\
rlm@475	1822 z_1 & z_2 & z_3 & n_z \\
rlm@475	1823 1 & 1 & 1 & 1
rlm@475	1824 \end{bmatrix}
rlm@476	1825 $$
rlm@476	1826 #+END_LaTeX
rlm@475	1827
rlm@475	1828 Here, the first three columns of the matrix are the vertices of
rlm@475	1829 the triangle. The last column is the right-handed unit normal of
rlm@475	1830 the triangle.
rlm@475	1831
rlm@476	1832 With two triangles $T_{1}$ and $T_{2}$ each expressed as a
rlm@476	1833 matrix like above, the affine transform from $T_{1}$ to $T_{2}$
rlm@476	1834 is $T_{2}T_{1}^{-1}$.
rlm@475	1835
rlm@475	1836 The clojure code below recapitulates the formulas above, using
rlm@475	1837 jMonkeyEngine's =Matrix4f= objects, which can describe any affine
rlm@475	1838 transformation.
rlm@474	1839
rlm@517	1840 #+caption: Program to interpret triangles as affine transforms.
rlm@475	1841 #+name: triangle-affine
rlm@475	1842 #+begin_listing clojure
rlm@475	1843 #+BEGIN_SRC clojure
rlm@474	1844 (defn triangle->matrix4f
rlm@474	1845 "Converts the triangle into a 4x4 matrix: The first three columns
rlm@474	1846 contain the vertices of the triangle; the last contains the unit
rlm@474	1847 normal of the triangle. The bottom row is filled with 1s."
rlm@474	1848 [#^Triangle t]
rlm@474	1849 (let [mat (Matrix4f.)
rlm@474	1850 [vert-1 vert-2 vert-3]
rlm@474	1851 (mapv #(.get t %) (range 3))
rlm@474	1852 unit-normal (do (.calculateNormal t)(.getNormal t))
rlm@474	1853 vertices [vert-1 vert-2 vert-3 unit-normal]]
rlm@474	1854 (dorun
rlm@474	1855 (for [row (range 4) col (range 3)]
rlm@474	1856 (do
rlm@474	1857 (.set mat col row (.get (vertices row) col))
rlm@474	1858 (.set mat 3 row 1)))) mat))
rlm@474	1859
rlm@474	1860 (defn triangles->affine-transform
rlm@474	1861 "Returns the affine transformation that converts each vertex in the
rlm@474	1862 first triangle into the corresponding vertex in the second
rlm@474	1863 triangle."
rlm@474	1864 [#^Triangle tri-1 #^Triangle tri-2]
rlm@474	1865 (.mult
rlm@474	1866 (triangle->matrix4f tri-2)
rlm@474	1867 (.invert (triangle->matrix4f tri-1))))
rlm@475	1868 #+END_SRC
rlm@475	1869 #+end_listing
rlm@474	1870
rlm@477	1871 *** Triangle Boundaries
rlm@474	1872
rlm@474	1873 For efficiency's sake I will divide the tactile-profile image into
rlm@474	1874 small squares which inscribe each pixel-triangle, then extract the
rlm@474	1875 points which lie inside the triangle and map them to 3D-space using
rlm@474	1876 =triangle-transform= above. To do this I need a function,
rlm@474	1877 =convex-bounds= which finds the smallest box which inscribes a 2D
rlm@474	1878 triangle.
rlm@474	1879
rlm@474	1880 =inside-triangle?= determines whether a point is inside a triangle
rlm@474	1881 in 2D pixel-space.
rlm@474	1882
rlm@517	1883 #+caption: Program to efficiently determine point inclusion
rlm@475	1884 #+caption: in a triangle.
rlm@475	1885 #+name: in-triangle
rlm@475	1886 #+begin_listing clojure
rlm@475	1887 #+BEGIN_SRC clojure
rlm@474	1888 (defn convex-bounds
rlm@474	1889 "Returns the smallest square containing the given vertices, as a
rlm@474	1890 vector of integers [left top width height]."
rlm@474	1891 [verts]
rlm@474	1892 (let [xs (map first verts)
rlm@474	1893 ys (map second verts)
rlm@474	1894 x0 (Math/floor (apply min xs))
rlm@474	1895 y0 (Math/floor (apply min ys))
rlm@474	1896 x1 (Math/ceil (apply max xs))
rlm@474	1897 y1 (Math/ceil (apply max ys))]
rlm@474	1898 [x0 y0 (- x1 x0) (- y1 y0)]))
rlm@474	1899
rlm@474	1900 (defn same-side?
rlm@474	1901 "Given the points p1 and p2 and the reference point ref, is point p
rlm@474	1902 on the same side of the line that goes through p1 and p2 as ref is?"
rlm@474	1903 [p1 p2 ref p]
rlm@474	1904 (<=
rlm@474	1905 0
rlm@474	1906 (.dot
rlm@474	1907 (.cross (.subtract p2 p1) (.subtract p p1))
rlm@474	1908 (.cross (.subtract p2 p1) (.subtract ref p1)))))
rlm@474	1909
rlm@474	1910 (defn inside-triangle?
rlm@474	1911 "Is the point inside the triangle?"
rlm@474	1912 {:author "Dylan Holmes"}
rlm@474	1913 [#^Triangle tri #^Vector3f p]
rlm@474	1914 (let [[vert-1 vert-2 vert-3] [(.get1 tri) (.get2 tri) (.get3 tri)]]
rlm@474	1915 (and
rlm@474	1916 (same-side? vert-1 vert-2 vert-3 p)
rlm@474	1917 (same-side? vert-2 vert-3 vert-1 p)
rlm@474	1918 (same-side? vert-3 vert-1 vert-2 p))))
rlm@475	1919 #+END_SRC
rlm@475	1920 #+end_listing
rlm@474	1921
rlm@477	1922 *** Feeler Coordinates
rlm@474	1923
rlm@475	1924 The triangle-related functions above make short work of
rlm@475	1925 calculating the positions and orientations of each feeler in
rlm@475	1926 world-space.
rlm@474	1927
rlm@475	1928 #+caption: Program to get the coordinates of ``feelers '' in
rlm@475	1929 #+caption: both world and UV-coordinates.
rlm@475	1930 #+name: feeler-coordinates
rlm@475	1931 #+begin_listing clojure
rlm@475	1932 #+BEGIN_SRC clojure
rlm@474	1933 (defn feeler-pixel-coords
rlm@474	1934 "Returns the coordinates of the feelers in pixel space in lists, one
rlm@474	1935 list for each triangle, ordered in the same way as (triangles) and
rlm@474	1936 (pixel-triangles)."
rlm@474	1937 [#^Geometry geo image]
rlm@474	1938 (map
rlm@474	1939 (fn [pixel-triangle]
rlm@474	1940 (filter
rlm@474	1941 (fn [coord]
rlm@474	1942 (inside-triangle? (->triangle pixel-triangle)
rlm@474	1943 (->vector3f coord)))
rlm@474	1944 (white-coordinates image (convex-bounds pixel-triangle))))
rlm@474	1945 (pixel-triangles geo image)))
rlm@474	1946
rlm@474	1947 (defn feeler-world-coords
rlm@474	1948 "Returns the coordinates of the feelers in world space in lists, one
rlm@474	1949 list for each triangle, ordered in the same way as (triangles) and
rlm@474	1950 (pixel-triangles)."
rlm@474	1951 [#^Geometry geo image]
rlm@474	1952 (let [transforms
rlm@474	1953 (map #(triangles->affine-transform
rlm@474	1954 (->triangle %1) (->triangle %2))
rlm@474	1955 (pixel-triangles geo image)
rlm@474	1956 (triangles geo))]
rlm@474	1957 (map (fn [transform coords]
rlm@474	1958 (map #(.mult transform (->vector3f %)) coords))
rlm@474	1959 transforms (feeler-pixel-coords geo image))))
rlm@475	1960 #+END_SRC
rlm@475	1961 #+end_listing
rlm@474	1962
rlm@475	1963 #+caption: Program to get the position of the base and tip of
rlm@475	1964 #+caption: each ``feeler''
rlm@475	1965 #+name: feeler-tips
rlm@475	1966 #+begin_listing clojure
rlm@475	1967 #+BEGIN_SRC clojure
rlm@474	1968 (defn feeler-origins
rlm@474	1969 "The world space coordinates of the root of each feeler."
rlm@474	1970 [#^Geometry geo image]
rlm@474	1971 (reduce concat (feeler-world-coords geo image)))
rlm@474	1972
rlm@474	1973 (defn feeler-tips
rlm@474	1974 "The world space coordinates of the tip of each feeler."
rlm@474	1975 [#^Geometry geo image]
rlm@474	1976 (let [world-coords (feeler-world-coords geo image)
rlm@474	1977 normals
rlm@474	1978 (map
rlm@474	1979 (fn [triangle]
rlm@474	1980 (.calculateNormal triangle)
rlm@474	1981 (.clone (.getNormal triangle)))
rlm@474	1982 (map ->triangle (triangles geo)))]
rlm@474	1983
rlm@474	1984 (mapcat (fn [origins normal]
rlm@474	1985 (map #(.add % normal) origins))
rlm@474	1986 world-coords normals)))
rlm@474	1987
rlm@474	1988 (defn touch-topology
rlm@474	1989 [#^Geometry geo image]
rlm@474	1990 (collapse (reduce concat (feeler-pixel-coords geo image))))
rlm@475	1991 #+END_SRC
rlm@475	1992 #+end_listing
rlm@474	1993
rlm@477	1994 *** Simulated Touch
rlm@474	1995
rlm@475	1996 Now that the functions to construct feelers are complete,
rlm@475	1997 =touch-kernel= generates functions to be called from within a
rlm@475	1998 simulation that perform the necessary physics collisions to
rlm@475	1999 collect tactile data, and =touch!= recursively applies it to every
rlm@475	2000 node in the creature.
rlm@474	2001
rlm@475	2002 #+caption: Efficient program to transform a ray from
rlm@475	2003 #+caption: one position to another.
rlm@475	2004 #+name: set-ray
rlm@475	2005 #+begin_listing clojure
rlm@475	2006 #+BEGIN_SRC clojure
rlm@474	2007 (defn set-ray [#^Ray ray #^Matrix4f transform
rlm@474	2008 #^Vector3f origin #^Vector3f tip]
rlm@474	2009 ;; Doing everything locally reduces garbage collection by enough to
rlm@474	2010 ;; be worth it.
rlm@474	2011 (.mult transform origin (.getOrigin ray))
rlm@474	2012 (.mult transform tip (.getDirection ray))
rlm@474	2013 (.subtractLocal (.getDirection ray) (.getOrigin ray))
rlm@474	2014 (.normalizeLocal (.getDirection ray)))
rlm@475	2015 #+END_SRC
rlm@475	2016 #+end_listing
rlm@474	2017
rlm@475	2018 #+caption: This is the core of touch in =CORTEX= each feeler
rlm@475	2019 #+caption: follows the object it is bound to, reporting any
rlm@475	2020 #+caption: collisions that may happen.
rlm@475	2021 #+name: touch-kernel
rlm@475	2022 #+begin_listing clojure
rlm@475	2023 #+BEGIN_SRC clojure
rlm@474	2024 (defn touch-kernel
rlm@474	2025 "Constructs a function which will return tactile sensory data from
rlm@474	2026 'geo when called from inside a running simulation"
rlm@474	2027 [#^Geometry geo]
rlm@474	2028 (if-let
rlm@474	2029 [profile (tactile-sensor-profile geo)]
rlm@474	2030 (let [ray-reference-origins (feeler-origins geo profile)
rlm@474	2031 ray-reference-tips (feeler-tips geo profile)
rlm@474	2032 ray-length (tactile-scale geo)
rlm@474	2033 current-rays (map (fn [_] (Ray.)) ray-reference-origins)
rlm@474	2034 topology (touch-topology geo profile)
rlm@474	2035 correction (float (* ray-length -0.2))]
rlm@474	2036 ;; slight tolerance for very close collisions.
rlm@474	2037 (dorun
rlm@474	2038 (map (fn [origin tip]
rlm@474	2039 (.addLocal origin (.mult (.subtract tip origin)
rlm@474	2040 correction)))
rlm@474	2041 ray-reference-origins ray-reference-tips))
rlm@474	2042 (dorun (map #(.setLimit % ray-length) current-rays))
rlm@474	2043 (fn [node]
rlm@474	2044 (let [transform (.getWorldMatrix geo)]
rlm@474	2045 (dorun
rlm@474	2046 (map (fn [ray ref-origin ref-tip]
rlm@474	2047 (set-ray ray transform ref-origin ref-tip))
rlm@474	2048 current-rays ray-reference-origins
rlm@474	2049 ray-reference-tips))
rlm@474	2050 (vector
rlm@474	2051 topology
rlm@474	2052 (vec
rlm@474	2053 (for [ray current-rays]
rlm@474	2054 (do
rlm@474	2055 (let [results (CollisionResults.)]
rlm@474	2056 (.collideWith node ray results)
rlm@474	2057 (let [touch-objects
rlm@474	2058 (filter #(not (= geo (.getGeometry %)))
rlm@474	2059 results)
rlm@474	2060 limit (.getLimit ray)]
rlm@474	2061 [(if (empty? touch-objects)
rlm@474	2062 limit
rlm@474	2063 (let [response
rlm@474	2064 (apply min (map #(.getDistance %)
rlm@474	2065 touch-objects))]
rlm@474	2066 (FastMath/clamp
rlm@474	2067 (float
rlm@474	2068 (if (> response limit) (float 0.0)
rlm@474	2069 (+ response correction)))
rlm@474	2070 (float 0.0)
rlm@474	2071 limit)))
rlm@474	2072 limit])))))))))))
rlm@475	2073 #+END_SRC
rlm@475	2074 #+end_listing
rlm@474	2075
rlm@475	2076 Armed with the =touch!= function, =CORTEX= becomes capable of
rlm@475	2077 giving creatures a sense of touch. A simple test is to create a
rlm@517	2078 cube that is outfitted with a uniform distribution of touch
rlm@475	2079 sensors. It can feel the ground and any balls that it touches.
rlm@475	2080
rlm@475	2081 #+caption: =CORTEX= interface for creating touch in a simulated
rlm@475	2082 #+caption: creature.
rlm@475	2083 #+name: touch
rlm@475	2084 #+begin_listing clojure
rlm@475	2085 #+BEGIN_SRC clojure
rlm@474	2086 (defn touch!
rlm@474	2087 "Endow the creature with the sense of touch. Returns a sequence of
rlm@474	2088 functions, one for each body part with a tactile-sensor-profile,
rlm@474	2089 each of which when called returns sensory data for that body part."
rlm@474	2090 [#^Node creature]
rlm@474	2091 (filter
rlm@474	2092 (comp not nil?)
rlm@474	2093 (map touch-kernel
rlm@474	2094 (filter #(isa? (class %) Geometry)
rlm@474	2095 (node-seq creature)))))
rlm@475	2096 #+END_SRC
rlm@475	2097 #+end_listing
rlm@475	2098
rlm@475	2099 The tactile-sensor-profile image for the touch cube is a simple
rlm@517	2100 cross with a uniform distribution of touch sensors:
rlm@474	2101
rlm@475	2102 #+caption: The touch profile for the touch-cube. Each pure white
rlm@475	2103 #+caption: pixel defines a touch sensitive feeler.
rlm@475	2104 #+name: touch-cube-uv-map
rlm@495	2105 #+ATTR_LaTeX: :width 7cm
rlm@475	2106 [[./images/touch-profile.png]]
rlm@474	2107
rlm@517	2108 #+caption: The touch cube reacts to cannonballs. The black, red,
rlm@475	2109 #+caption: and white cross on the right is a visual display of
rlm@475	2110 #+caption: the creature's touch. White means that it is feeling
rlm@475	2111 #+caption: something strongly, black is not feeling anything,
rlm@475	2112 #+caption: and gray is in-between. The cube can feel both the
rlm@475	2113 #+caption: floor and the ball. Notice that when the ball causes
rlm@475	2114 #+caption: the cube to tip, that the bottom face can still feel
rlm@475	2115 #+caption: part of the ground.
rlm@518	2116 #+name: touch-cube-uv-map-2
rlm@475	2117 #+ATTR_LaTeX: :width 15cm
rlm@475	2118 [[./images/touch-cube.png]]
rlm@474	2119
rlm@511	2120 ** Proprioception provides knowledge of your own body's position
rlm@436	2121
rlm@479	2122 Close your eyes, and touch your nose with your right index finger.
rlm@479	2123 How did you do it? You could not see your hand, and neither your
rlm@479	2124 hand nor your nose could use the sense of touch to guide the path
rlm@479	2125 of your hand. There are no sound cues, and Taste and Smell
rlm@479	2126 certainly don't provide any help. You know where your hand is
rlm@479	2127 without your other senses because of Proprioception.
rlm@479	2128
rlm@479	2129 Humans can sometimes loose this sense through viral infections or
rlm@479	2130 damage to the spinal cord or brain, and when they do, they loose
rlm@479	2131 the ability to control their own bodies without looking directly at
rlm@479	2132 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	2133 Hat]] (\cite{man-wife-hat}), a woman named Christina looses this
rlm@518	2134 sense and has to learn how to move by carefully watching her arms
rlm@518	2135 and legs. She describes proprioception as the "eyes of the body,
rlm@518	2136 the way the body sees itself".
rlm@479	2137
rlm@479	2138 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	2139 spindles]], and the [[http://en.wikipedia.org/wiki/Golgi_tendon_organ][Golgi tendon organs]]. These measure the relative
rlm@479	2140 positions of each body part by monitoring muscle strain and length.
rlm@479	2141
rlm@479	2142 It's clear that this is a vital sense for fluid, graceful movement.
rlm@479	2143 It's also particularly easy to implement in jMonkeyEngine.
rlm@479	2144
rlm@479	2145 My simulated proprioception calculates the relative angles of each
rlm@479	2146 joint from the rest position defined in the blender file. This
rlm@479	2147 simulates the muscle-spindles and joint capsules. I will deal with
rlm@479	2148 Golgi tendon organs, which calculate muscle strain, in the next
rlm@479	2149 section.
rlm@479	2150
rlm@479	2151 *** Helper functions
rlm@479	2152
rlm@479	2153 =absolute-angle= calculates the angle between two vectors,
rlm@479	2154 relative to a third axis vector. This angle is the number of
rlm@479	2155 radians you have to move counterclockwise around the axis vector
rlm@479	2156 to get from the first to the second vector. It is not commutative
rlm@479	2157 like a normal dot-product angle is.
rlm@479	2158
rlm@479	2159 The purpose of these functions is to build a system of angle
rlm@517	2160 measurement that is biologically plausible.
rlm@479	2161
rlm@479	2162 #+caption: Program to measure angles along a vector
rlm@479	2163 #+name: helpers
rlm@479	2164 #+begin_listing clojure
rlm@479	2165 #+BEGIN_SRC clojure
rlm@479	2166 (defn right-handed?
rlm@479	2167 "true iff the three vectors form a right handed coordinate
rlm@479	2168 system. The three vectors do not have to be normalized or
rlm@479	2169 orthogonal."
rlm@479	2170 [vec1 vec2 vec3]
rlm@479	2171 (pos? (.dot (.cross vec1 vec2) vec3)))
rlm@479	2172
rlm@479	2173 (defn absolute-angle
rlm@479	2174 "The angle between 'vec1 and 'vec2 around 'axis. In the range
rlm@479	2175 [0 (* 2 Math/PI)]."
rlm@479	2176 [vec1 vec2 axis]
rlm@479	2177 (let [angle (.angleBetween vec1 vec2)]
rlm@479	2178 (if (right-handed? vec1 vec2 axis)
rlm@479	2179 angle (- (* 2 Math/PI) angle))))
rlm@479	2180 #+END_SRC
rlm@479	2181 #+end_listing
rlm@479	2182
rlm@479	2183 *** Proprioception Kernel
rlm@479	2184
rlm@479	2185 Given a joint, =proprioception-kernel= produces a function that
rlm@545	2186 calculates the Euler angles between the objects the joint
rlm@479	2187 connects. The only tricky part here is making the angles relative
rlm@479	2188 to the joint's initial ``straightness''.
rlm@479	2189
rlm@517	2190 #+caption: Program to return biologically reasonable proprioceptive
rlm@479	2191 #+caption: data for each joint.
rlm@479	2192 #+name: proprioception
rlm@479	2193 #+begin_listing clojure
rlm@479	2194 #+BEGIN_SRC clojure
rlm@479	2195 (defn proprioception-kernel
rlm@479	2196 "Returns a function which returns proprioceptive sensory data when
rlm@479	2197 called inside a running simulation."
rlm@479	2198 [#^Node parts #^Node joint]
rlm@479	2199 (let [[obj-a obj-b] (joint-targets parts joint)
rlm@479	2200 joint-rot (.getWorldRotation joint)
rlm@479	2201 x0 (.mult joint-rot Vector3f/UNIT_X)
rlm@479	2202 y0 (.mult joint-rot Vector3f/UNIT_Y)
rlm@479	2203 z0 (.mult joint-rot Vector3f/UNIT_Z)]
rlm@479	2204 (fn []
rlm@479	2205 (let [rot-a (.clone (.getWorldRotation obj-a))
rlm@479	2206 rot-b (.clone (.getWorldRotation obj-b))
rlm@479	2207 x (.mult rot-a x0)
rlm@479	2208 y (.mult rot-a y0)
rlm@479	2209 z (.mult rot-a z0)
rlm@479	2210
rlm@479	2211 X (.mult rot-b x0)
rlm@479	2212 Y (.mult rot-b y0)
rlm@479	2213 Z (.mult rot-b z0)
rlm@479	2214 heading (Math/atan2 (.dot X z) (.dot X x))
rlm@479	2215 pitch (Math/atan2 (.dot X y) (.dot X x))
rlm@479	2216
rlm@479	2217 ;; rotate x-vector back to origin
rlm@479	2218 reverse
rlm@479	2219 (doto (Quaternion.)
rlm@479	2220 (.fromAngleAxis
rlm@479	2221 (.angleBetween X x)
rlm@479	2222 (let [cross (.normalize (.cross X x))]
rlm@479	2223 (if (= 0 (.length cross)) y cross))))
rlm@479	2224 roll (absolute-angle (.mult reverse Y) y x)]
rlm@479	2225 [heading pitch roll]))))
rlm@479	2226
rlm@479	2227 (defn proprioception!
rlm@479	2228 "Endow the creature with the sense of proprioception. Returns a
rlm@479	2229 sequence of functions, one for each child of the \"joints\" node in
rlm@479	2230 the creature, which each report proprioceptive information about
rlm@479	2231 that joint."
rlm@479	2232 [#^Node creature]
rlm@479	2233 ;; extract the body's joints
rlm@479	2234 (let [senses (map (partial proprioception-kernel creature)
rlm@479	2235 (joints creature))]
rlm@479	2236 (fn []
rlm@479	2237 (map #(%) senses))))
rlm@479	2238 #+END_SRC
rlm@479	2239 #+end_listing
rlm@479	2240
rlm@479	2241 =proprioception!= maps =proprioception-kernel= across all the
rlm@479	2242 joints of the creature. It uses the same list of joints that
rlm@479	2243 =joints= uses. Proprioception is the easiest sense to implement in
rlm@479	2244 =CORTEX=, and it will play a crucial role when efficiently
rlm@479	2245 implementing empathy.
rlm@479	2246
rlm@479	2247 #+caption: In the upper right corner, the three proprioceptive
rlm@479	2248 #+caption: angle measurements are displayed. Red is yaw, Green is
rlm@479	2249 #+caption: pitch, and White is roll.
rlm@479	2250 #+name: proprio
rlm@479	2251 #+ATTR_LaTeX: :width 11cm
rlm@479	2252 [[./images/proprio.png]]
rlm@479	2253
rlm@511	2254 ** Muscles contain both sensors and effectors
rlm@481	2255
rlm@481	2256 Surprisingly enough, terrestrial creatures only move by using
rlm@481	2257 torque applied about their joints. There's not a single straight
rlm@481	2258 line of force in the human body at all! (A straight line of force
rlm@481	2259 would correspond to some sort of jet or rocket propulsion.)
rlm@481	2260
rlm@481	2261 In humans, muscles are composed of muscle fibers which can contract
rlm@481	2262 to exert force. The muscle fibers which compose a muscle are
rlm@481	2263 partitioned into discrete groups which are each controlled by a
rlm@481	2264 single alpha motor neuron. A single alpha motor neuron might
rlm@481	2265 control as little as three or as many as one thousand muscle
rlm@481	2266 fibers. When the alpha motor neuron is engaged by the spinal cord,
rlm@481	2267 it activates all of the muscle fibers to which it is attached. The
rlm@481	2268 spinal cord generally engages the alpha motor neurons which control
rlm@481	2269 few muscle fibers before the motor neurons which control many
rlm@481	2270 muscle fibers. This recruitment strategy allows for precise
rlm@481	2271 movements at low strength. The collection of all motor neurons that
rlm@481	2272 control a muscle is called the motor pool. The brain essentially
rlm@481	2273 says "activate 30% of the motor pool" and the spinal cord recruits
rlm@481	2274 motor neurons until 30% are activated. Since the distribution of
rlm@481	2275 power among motor neurons is unequal and recruitment goes from
rlm@481	2276 weakest to strongest, the first 30% of the motor pool might be 5%
rlm@481	2277 of the strength of the muscle.
rlm@481	2278
rlm@481	2279 My simulated muscles follow a similar design: Each muscle is
rlm@481	2280 defined by a 1-D array of numbers (the "motor pool"). Each entry in
rlm@481	2281 the array represents a motor neuron which controls a number of
rlm@481	2282 muscle fibers equal to the value of the entry. Each muscle has a
rlm@481	2283 scalar strength factor which determines the total force the muscle
rlm@481	2284 can exert when all motor neurons are activated. The effector
rlm@481	2285 function for a muscle takes a number to index into the motor pool,
rlm@481	2286 and then "activates" all the motor neurons whose index is lower or
rlm@481	2287 equal to the number. Each motor-neuron will apply force in
rlm@481	2288 proportion to its value in the array. Lower values cause less
rlm@481	2289 force. The lower values can be put at the "beginning" of the 1-D
rlm@481	2290 array to simulate the layout of actual human muscles, which are
rlm@481	2291 capable of more precise movements when exerting less force. Or, the
rlm@481	2292 motor pool can simulate more exotic recruitment strategies which do
rlm@481	2293 not correspond to human muscles.
rlm@481	2294
rlm@481	2295 This 1D array is defined in an image file for ease of
rlm@481	2296 creation/visualization. Here is an example muscle profile image.
rlm@481	2297
rlm@481	2298 #+caption: A muscle profile image that describes the strengths
rlm@481	2299 #+caption: of each motor neuron in a muscle. White is weakest
rlm@481	2300 #+caption: and dark red is strongest. This particular pattern
rlm@481	2301 #+caption: has weaker motor neurons at the beginning, just
rlm@481	2302 #+caption: like human muscle.
rlm@481	2303 #+name: muscle-recruit
rlm@481	2304 #+ATTR_LaTeX: :width 7cm
rlm@481	2305 [[./images/basic-muscle.png]]
rlm@481	2306
rlm@481	2307 *** Muscle meta-data
rlm@481	2308
rlm@481	2309 #+caption: Program to deal with loading muscle data from a blender
rlm@481	2310 #+caption: file's metadata.
rlm@481	2311 #+name: motor-pool
rlm@481	2312 #+begin_listing clojure
rlm@481	2313 #+BEGIN_SRC clojure
rlm@481	2314 (defn muscle-profile-image
rlm@481	2315 "Get the muscle-profile image from the node's blender meta-data."
rlm@481	2316 [#^Node muscle]
rlm@481	2317 (if-let [image (meta-data muscle "muscle")]
rlm@481	2318 (load-image image)))
rlm@481	2319
rlm@481	2320 (defn muscle-strength
rlm@481	2321 "Return the strength of this muscle, or 1 if it is not defined."
rlm@481	2322 [#^Node muscle]
rlm@481	2323 (if-let [strength (meta-data muscle "strength")]
rlm@481	2324 strength 1))
rlm@481	2325
rlm@481	2326 (defn motor-pool
rlm@481	2327 "Return a vector where each entry is the strength of the \"motor
rlm@481	2328 neuron\" at that part in the muscle."
rlm@481	2329 [#^Node muscle]
rlm@481	2330 (let [profile (muscle-profile-image muscle)]
rlm@481	2331 (vec
rlm@481	2332 (let [width (.getWidth profile)]
rlm@481	2333 (for [x (range width)]
rlm@481	2334 (- 255
rlm@481	2335 (bit-and
rlm@481	2336 0x0000FF
rlm@481	2337 (.getRGB profile x 0))))))))
rlm@481	2338 #+END_SRC
rlm@481	2339 #+end_listing
rlm@481	2340
rlm@481	2341 Of note here is =motor-pool= which interprets the muscle-profile
rlm@481	2342 image in a way that allows me to use gradients between white and
rlm@481	2343 red, instead of shades of gray as I've been using for all the
rlm@481	2344 other senses. This is purely an aesthetic touch.
rlm@481	2345
rlm@481	2346 *** Creating muscles
rlm@481	2347
rlm@517	2348 #+caption: This is the core movement function in =CORTEX=, which
rlm@481	2349 #+caption: implements muscles that report on their activation.
rlm@481	2350 #+name: muscle-kernel
rlm@481	2351 #+begin_listing clojure
rlm@481	2352 #+BEGIN_SRC clojure
rlm@481	2353 (defn movement-kernel
rlm@481	2354 "Returns a function which when called with a integer value inside a
rlm@481	2355 running simulation will cause movement in the creature according
rlm@481	2356 to the muscle's position and strength profile. Each function
rlm@481	2357 returns the amount of force applied / max force."
rlm@481	2358 [#^Node creature #^Node muscle]
rlm@481	2359 (let [target (closest-node creature muscle)
rlm@481	2360 axis
rlm@481	2361 (.mult (.getWorldRotation muscle) Vector3f/UNIT_Y)
rlm@481	2362 strength (muscle-strength muscle)
rlm@481	2363
rlm@481	2364 pool (motor-pool muscle)
rlm@481	2365 pool-integral (reductions + pool)
rlm@481	2366 forces
rlm@481	2367 (vec (map #(float (* strength (/ % (last pool-integral))))
rlm@481	2368 pool-integral))
rlm@481	2369 control (.getControl target RigidBodyControl)]
rlm@481	2370 ;;(println-repl (.getName target) axis)
rlm@481	2371 (fn [n]
rlm@481	2372 (let [pool-index (max 0 (min n (dec (count pool))))
rlm@481	2373 force (forces pool-index)]
rlm@481	2374 (.applyTorque control (.mult axis force))
rlm@481	2375 (float (/ force strength))))))
rlm@481	2376
rlm@481	2377 (defn movement!
rlm@481	2378 "Endow the creature with the power of movement. Returns a sequence
rlm@481	2379 of functions, each of which accept an integer value and will
rlm@481	2380 activate their corresponding muscle."
rlm@481	2381 [#^Node creature]
rlm@481	2382 (for [muscle (muscles creature)]
rlm@481	2383 (movement-kernel creature muscle)))
rlm@481	2384 #+END_SRC
rlm@481	2385 #+end_listing
rlm@481	2386
rlm@481	2387
rlm@531	2388 =movement-kernel= creates a function that controls the movement
rlm@525	2389 of the nearest physical node to the muscle node. The muscle exerts
rlm@525	2390 a rotational force dependent on it's orientation to the object in
rlm@525	2391 the blender file. The function returned by =movement-kernel= is
rlm@525	2392 also a sense function: it returns the percent of the total muscle
rlm@525	2393 strength that is currently being employed. This is analogous to
rlm@525	2394 muscle tension in humans and completes the sense of proprioception
rlm@525	2395 begun in the last section.
rlm@488	2396
rlm@507	2397 ** =CORTEX= brings complex creatures to life!
rlm@483	2398
rlm@483	2399 The ultimate test of =CORTEX= is to create a creature with the full
rlm@483	2400 gamut of senses and put it though its paces.
rlm@483	2401
rlm@483	2402 With all senses enabled, my right hand model looks like an
rlm@483	2403 intricate marionette hand with several strings for each finger:
rlm@483	2404
rlm@483	2405 #+caption: View of the hand model with all sense nodes. You can see
rlm@517	2406 #+caption: the joint, muscle, ear, and eye nodes here.
rlm@483	2407 #+name: hand-nodes-1
rlm@483	2408 #+ATTR_LaTeX: :width 11cm
rlm@483	2409 [[./images/hand-with-all-senses2.png]]
rlm@483	2410
rlm@483	2411 #+caption: An alternate view of the hand.
rlm@483	2412 #+name: hand-nodes-2
rlm@484	2413 #+ATTR_LaTeX: :width 15cm
rlm@484	2414 [[./images/hand-with-all-senses3.png]]
rlm@484	2415
rlm@484	2416 With the hand fully rigged with senses, I can run it though a test
rlm@484	2417 that will test everything.
rlm@484	2418
rlm@517	2419 #+caption: A full test of the hand with all senses. Note especially
rlm@495	2420 #+caption: the interactions the hand has with itself: it feels
rlm@484	2421 #+caption: its own palm and fingers, and when it curls its fingers,
rlm@484	2422 #+caption: it sees them with its eye (which is located in the center
rlm@484	2423 #+caption: of the palm. The red block appears with a pure tone sound.
rlm@484	2424 #+caption: The hand then uses its muscles to launch the cube!
rlm@484	2425 #+name: integration
rlm@484	2426 #+ATTR_LaTeX: :width 16cm
rlm@484	2427 [[./images/integration.png]]
rlm@436	2428
rlm@517	2429 ** =CORTEX= enables many possibilities for further research
rlm@485	2430
rlm@485	2431 Often times, the hardest part of building a system involving
rlm@485	2432 creatures is dealing with physics and graphics. =CORTEX= removes
rlm@485	2433 much of this initial difficulty and leaves researchers free to
rlm@485	2434 directly pursue their ideas. I hope that even undergrads with a
rlm@485	2435 passing curiosity about simulated touch or creature evolution will
rlm@485	2436 be able to use cortex for experimentation. =CORTEX= is a completely
rlm@485	2437 simulated world, and far from being a disadvantage, its simulated
rlm@485	2438 nature enables you to create senses and creatures that would be
rlm@485	2439 impossible to make in the real world.
rlm@485	2440
rlm@485	2441 While not by any means a complete list, here are some paths
rlm@485	2442 =CORTEX= is well suited to help you explore:
rlm@485	2443
rlm@485	2444 - Empathy :: my empathy program leaves many areas for
rlm@485	2445 improvement, among which are using vision to infer
rlm@485	2446 proprioception and looking up sensory experience with imagined
rlm@485	2447 vision, touch, and sound.
rlm@547	2448 - Evolution :: Karl Sims created a rich environment for simulating
rlm@547	2449 the evolution of creatures on a Connection Machine
rlm@547	2450 (\cite{sims-evolving-creatures}). Today, this can be redone
rlm@547	2451 and expanded with =CORTEX= on an ordinary computer.
rlm@485	2452 - Exotic senses :: Cortex enables many fascinating senses that are
rlm@485	2453 not possible to build in the real world. For example,
rlm@485	2454 telekinesis is an interesting avenue to explore. You can also
rlm@485	2455 make a ``semantic'' sense which looks up metadata tags on
rlm@485	2456 objects in the environment the metadata tags might contain
rlm@485	2457 other sensory information.
rlm@485	2458 - Imagination via subworlds :: this would involve a creature with
rlm@485	2459 an effector which creates an entire new sub-simulation where
rlm@485	2460 the creature has direct control over placement/creation of
rlm@485	2461 objects via simulated telekinesis. The creature observes this
rlm@547	2462 sub-world through its normal senses and uses its observations
rlm@485	2463 to make predictions about its top level world.
rlm@485	2464 - Simulated prescience :: step the simulation forward a few ticks,
rlm@485	2465 gather sensory data, then supply this data for the creature as
rlm@485	2466 one of its actual senses. The cost of prescience is slowing
rlm@485	2467 the simulation down by a factor proportional to however far
rlm@485	2468 you want the entities to see into the future. What happens
rlm@485	2469 when two evolved creatures that can each see into the future
rlm@485	2470 fight each other?
rlm@485	2471 - Swarm creatures :: Program a group of creatures that cooperate
rlm@485	2472 with each other. Because the creatures would be simulated, you
rlm@485	2473 could investigate computationally complex rules of behavior
rlm@485	2474 which still, from the group's point of view, would happen in
rlm@547	2475 real time. Interactions could be as simple as cellular
rlm@485	2476 organisms communicating via flashing lights, or as complex as
rlm@485	2477 humanoids completing social tasks, etc.
rlm@547	2478 - =HACKER= for writing muscle-control programs :: Presented with a
rlm@547	2479 low-level muscle control / sense API, generate higher level
rlm@485	2480 programs for accomplishing various stated goals. Example goals
rlm@485	2481 might be "extend all your fingers" or "move your hand into the
rlm@485	2482 area with blue light" or "decrease the angle of this joint".
rlm@485	2483 It would be like Sussman's HACKER, except it would operate
rlm@485	2484 with much more data in a more realistic world. Start off with
rlm@485	2485 "calisthenics" to develop subroutines over the motor control
rlm@547	2486 API. The low level programming code might be a turning machine
rlm@547	2487 that could develop programs to iterate over a "tape" where
rlm@547	2488 each entry in the tape could control recruitment of the fibers
rlm@547	2489 in a muscle.
rlm@547	2490 - Sense fusion :: There is much work to be done on sense
rlm@485	2491 integration -- building up a coherent picture of the world and
rlm@547	2492 the things in it. With =CORTEX= as a base, you can explore
rlm@485	2493 concepts like self-organizing maps or cross modal clustering
rlm@485	2494 in ways that have never before been tried.
rlm@485	2495 - Inverse kinematics :: experiments in sense guided motor control
rlm@485	2496 are easy given =CORTEX='s support -- you can get right to the
rlm@485	2497 hard control problems without worrying about physics or
rlm@485	2498 senses.
rlm@485	2499
rlm@525	2500 \newpage
rlm@525	2501
rlm@515	2502 * =EMPATH=: action recognition in a simulated worm
rlm@435	2503
rlm@449	2504 Here I develop a computational model of empathy, using =CORTEX= as a
rlm@449	2505 base. Empathy in this context is the ability to observe another
rlm@449	2506 creature and infer what sorts of sensations that creature is
rlm@449	2507 feeling. My empathy algorithm involves multiple phases. First is
rlm@449	2508 free-play, where the creature moves around and gains sensory
rlm@449	2509 experience. From this experience I construct a representation of the
rlm@449	2510 creature's sensory state space, which I call \Phi-space. Using
rlm@449	2511 \Phi-space, I construct an efficient function which takes the
rlm@449	2512 limited data that comes from observing another creature and enriches
rlm@525	2513 it with a full compliment of imagined sensory data. I can then use
rlm@525	2514 the imagined sensory data to recognize what the observed creature is
rlm@449	2515 doing and feeling, using straightforward embodied action predicates.
rlm@449	2516 This is all demonstrated with using a simple worm-like creature, and
rlm@449	2517 recognizing worm-actions based on limited data.
rlm@449	2518
rlm@449	2519 #+caption: Here is the worm with which we will be working.
rlm@449	2520 #+caption: It is composed of 5 segments. Each segment has a
rlm@449	2521 #+caption: pair of extensor and flexor muscles. Each of the
rlm@449	2522 #+caption: worm's four joints is a hinge joint which allows
rlm@451	2523 #+caption: about 30 degrees of rotation to either side. Each segment
rlm@449	2524 #+caption: of the worm is touch-capable and has a uniform
rlm@449	2525 #+caption: distribution of touch sensors on each of its faces.
rlm@449	2526 #+caption: Each joint has a proprioceptive sense to detect
rlm@449	2527 #+caption: relative positions. The worm segments are all the
rlm@449	2528 #+caption: same except for the first one, which has a much
rlm@449	2529 #+caption: higher weight than the others to allow for easy
rlm@449	2530 #+caption: manual motor control.
rlm@449	2531 #+name: basic-worm-view
rlm@449	2532 #+ATTR_LaTeX: :width 10cm
rlm@449	2533 [[./images/basic-worm-view.png]]
rlm@449	2534
rlm@449	2535 #+caption: Program for reading a worm from a blender file and
rlm@449	2536 #+caption: outfitting it with the senses of proprioception,
rlm@449	2537 #+caption: touch, and the ability to move, as specified in the
rlm@449	2538 #+caption: blender file.
rlm@449	2539 #+name: get-worm
rlm@449	2540 #+begin_listing clojure
rlm@449	2541 #+begin_src clojure
rlm@449	2542 (defn worm []
rlm@449	2543 (let [model (load-blender-model "Models/worm/worm.blend")]
rlm@449	2544 {:body (doto model (body!))
rlm@449	2545 :touch (touch! model)
rlm@449	2546 :proprioception (proprioception! model)
rlm@449	2547 :muscles (movement! model)}))
rlm@449	2548 #+end_src
rlm@449	2549 #+end_listing
rlm@452	2550
rlm@531	2551 ** Embodiment factors action recognition into manageable parts
rlm@435	2552
rlm@449	2553 Using empathy, I divide the problem of action recognition into a
rlm@449	2554 recognition process expressed in the language of a full compliment
rlm@517	2555 of senses, and an imaginative process that generates full sensory
rlm@449	2556 data from partial sensory data. Splitting the action recognition
rlm@449	2557 problem in this manner greatly reduces the total amount of work to
rlm@517	2558 recognize actions: The imaginative process is mostly just matching
rlm@449	2559 previous experience, and the recognition process gets to use all
rlm@449	2560 the senses to directly describe any action.
rlm@449	2561
rlm@436	2562 ** Action recognition is easy with a full gamut of senses
rlm@435	2563
rlm@449	2564 Embodied representations using multiple senses such as touch,
rlm@545	2565 proprioception, and muscle tension turns out be exceedingly
rlm@525	2566 efficient at describing body-centered actions. It is the right
rlm@525	2567 language for the job. For example, it takes only around 5 lines of
rlm@525	2568 LISP code to describe the action of curling using embodied
rlm@451	2569 primitives. It takes about 10 lines to describe the seemingly
rlm@449	2570 complicated action of wiggling.
rlm@449	2571
rlm@449	2572 The following action predicates each take a stream of sensory
rlm@449	2573 experience, observe however much of it they desire, and decide
rlm@449	2574 whether the worm is doing the action they describe. =curled?=
rlm@449	2575 relies on proprioception, =resting?= relies on touch, =wiggling?=
rlm@517	2576 relies on a Fourier analysis of muscle contraction, and
rlm@525	2577 =grand-circle?= relies on touch and reuses =curled?= in its
rlm@525	2578 definition, showing how embodied predicates can be composed.
rlm@449	2579
rlm@530	2580
rlm@449	2581 #+caption: Program for detecting whether the worm is curled. This is the
rlm@449	2582 #+caption: simplest action predicate, because it only uses the last frame
rlm@449	2583 #+caption: of sensory experience, and only uses proprioceptive data. Even
rlm@449	2584 #+caption: this simple predicate, however, is automatically frame
rlm@530	2585 #+caption: independent and ignores vermopomorphic\protect\footnotemark
rlm@532	2586 #+caption: \space differences such as worm textures and colors.
rlm@449	2587 #+name: curled
rlm@509	2588 #+begin_listing clojure
rlm@449	2589 #+begin_src clojure
rlm@449	2590 (defn curled?
rlm@449	2591 "Is the worm curled up?"
rlm@449	2592 [experiences]
rlm@449	2593 (every?
rlm@449	2594 (fn [[_ _ bend]]
rlm@449	2595 (> (Math/sin bend) 0.64))
rlm@449	2596 (:proprioception (peek experiences))))
rlm@449	2597 #+end_src
rlm@449	2598 #+end_listing
rlm@530	2599
rlm@530	2600 #+BEGIN_LaTeX
rlm@530	2601 \footnotetext{Like \emph{anthropomorphic} except for worms instead of humans.}
rlm@530	2602 #+END_LaTeX
rlm@449	2603
rlm@449	2604 #+caption: Program for summarizing the touch information in a patch
rlm@449	2605 #+caption: of skin.
rlm@449	2606 #+name: touch-summary
rlm@509	2607 #+begin_listing clojure
rlm@449	2608 #+begin_src clojure
rlm@449	2609 (defn contact
rlm@449	2610 "Determine how much contact a particular worm segment has with
rlm@449	2611 other objects. Returns a value between 0 and 1, where 1 is full
rlm@449	2612 contact and 0 is no contact."
rlm@449	2613 [touch-region [coords contact :as touch]]
rlm@449	2614 (-> (zipmap coords contact)
rlm@449	2615 (select-keys touch-region)
rlm@449	2616 (vals)
rlm@449	2617 (#(map first %))
rlm@449	2618 (average)
rlm@449	2619 (* 10)
rlm@449	2620 (- 1)
rlm@449	2621 (Math/abs)))
rlm@449	2622 #+end_src
rlm@449	2623 #+end_listing
rlm@449	2624
rlm@449	2625
rlm@449	2626 #+caption: Program for detecting whether the worm is at rest. This program
rlm@449	2627 #+caption: uses a summary of the tactile information from the underbelly
rlm@449	2628 #+caption: of the worm, and is only true if every segment is touching the
rlm@449	2629 #+caption: floor. Note that this function contains no references to
rlm@517	2630 #+caption: proprioception at all.
rlm@449	2631 #+name: resting
rlm@452	2632 #+begin_listing clojure
rlm@449	2633 #+begin_src clojure
rlm@449	2634 (def worm-segment-bottom (rect-region [8 15] [14 22]))
rlm@449	2635
rlm@449	2636 (defn resting?
rlm@449	2637 "Is the worm resting on the ground?"
rlm@449	2638 [experiences]
rlm@449	2639 (every?
rlm@449	2640 (fn [touch-data]
rlm@449	2641 (< 0.9 (contact worm-segment-bottom touch-data)))
rlm@449	2642 (:touch (peek experiences))))
rlm@449	2643 #+end_src
rlm@449	2644 #+end_listing
rlm@449	2645
rlm@449	2646 #+caption: Program for detecting whether the worm is curled up into a
rlm@449	2647 #+caption: full circle. Here the embodied approach begins to shine, as
rlm@449	2648 #+caption: I am able to both use a previous action predicate (=curled?=)
rlm@449	2649 #+caption: as well as the direct tactile experience of the head and tail.
rlm@449	2650 #+name: grand-circle
rlm@452	2651 #+begin_listing clojure
rlm@449	2652 #+begin_src clojure
rlm@449	2653 (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))
rlm@449	2654
rlm@449	2655 (def worm-segment-top-tip (rect-region [0 15] [7 22]))
rlm@449	2656
rlm@449	2657 (defn grand-circle?
rlm@449	2658 "Does the worm form a majestic circle (one end touching the other)?"
rlm@449	2659 [experiences]
rlm@449	2660 (and (curled? experiences)
rlm@449	2661 (let [worm-touch (:touch (peek experiences))
rlm@449	2662 tail-touch (worm-touch 0)
rlm@449	2663 head-touch (worm-touch 4)]
rlm@449	2664 (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
rlm@449	2665 (< 0.55 (contact worm-segment-top-tip head-touch))))))
rlm@449	2666 #+end_src
rlm@449	2667 #+end_listing
rlm@449	2668
rlm@449	2669
rlm@449	2670 #+caption: Program for detecting whether the worm has been wiggling for
rlm@517	2671 #+caption: the last few frames. It uses a Fourier analysis of the muscle
rlm@449	2672 #+caption: contractions of the worm's tail to determine wiggling. This is
rlm@517	2673 #+caption: significant because there is no particular frame that clearly
rlm@449	2674 #+caption: indicates that the worm is wiggling --- only when multiple frames
rlm@449	2675 #+caption: are analyzed together is the wiggling revealed. Defining
rlm@449	2676 #+caption: wiggling this way also gives the worm an opportunity to learn
rlm@449	2677 #+caption: and recognize ``frustrated wiggling'', where the worm tries to
rlm@449	2678 #+caption: wiggle but can't. Frustrated wiggling is very visually different
rlm@449	2679 #+caption: from actual wiggling, but this definition gives it to us for free.
rlm@449	2680 #+name: wiggling
rlm@452	2681 #+begin_listing clojure
rlm@449	2682 #+begin_src clojure
rlm@449	2683 (defn fft [nums]
rlm@449	2684 (map
rlm@449	2685 #(.getReal %)
rlm@449	2686 (.transform
rlm@449	2687 (FastFourierTransformer. DftNormalization/STANDARD)
rlm@449	2688 (double-array nums) TransformType/FORWARD)))
rlm@449	2689
rlm@449	2690 (def indexed (partial map-indexed vector))
rlm@449	2691
rlm@449	2692 (defn max-indexed [s]
rlm@449	2693 (first (sort-by (comp - second) (indexed s))))
rlm@449	2694
rlm@449	2695 (defn wiggling?
rlm@449	2696 "Is the worm wiggling?"
rlm@449	2697 [experiences]
rlm@449	2698 (let [analysis-interval 0x40]
rlm@449	2699 (when (> (count experiences) analysis-interval)
rlm@449	2700 (let [a-flex 3
rlm@449	2701 a-ex 2
rlm@449	2702 muscle-activity
rlm@449	2703 (map :muscle (vector:last-n experiences analysis-interval))
rlm@449	2704 base-activity
rlm@449	2705 (map #(- (% a-flex) (% a-ex)) muscle-activity)]
rlm@449	2706 (= 2
rlm@449	2707 (first
rlm@449	2708 (max-indexed
rlm@449	2709 (map #(Math/abs %)
rlm@449	2710 (take 20 (fft base-activity))))))))))
rlm@449	2711 #+end_src
rlm@449	2712 #+end_listing
rlm@449	2713
rlm@449	2714 With these action predicates, I can now recognize the actions of
rlm@449	2715 the worm while it is moving under my control and I have access to
rlm@449	2716 all the worm's senses.
rlm@449	2717
rlm@449	2718 #+caption: Use the action predicates defined earlier to report on
rlm@449	2719 #+caption: what the worm is doing while in simulation.
rlm@449	2720 #+name: report-worm-activity
rlm@452	2721 #+begin_listing clojure
rlm@449	2722 #+begin_src clojure
rlm@449	2723 (defn debug-experience
rlm@449	2724 [experiences text]
rlm@449	2725 (cond
rlm@449	2726 (grand-circle? experiences) (.setText text "Grand Circle")
rlm@449	2727 (curled? experiences) (.setText text "Curled")
rlm@449	2728 (wiggling? experiences) (.setText text "Wiggling")
rlm@449	2729 (resting? experiences) (.setText text "Resting")))
rlm@449	2730 #+end_src
rlm@449	2731 #+end_listing
rlm@449	2732
rlm@449	2733 #+caption: Using =debug-experience=, the body-centered predicates
rlm@517	2734 #+caption: work together to classify the behavior of the worm.
rlm@451	2735 #+caption: the predicates are operating with access to the worm's
rlm@451	2736 #+caption: full sensory data.
rlm@449	2737 #+name: basic-worm-view
rlm@449	2738 #+ATTR_LaTeX: :width 10cm
rlm@449	2739 [[./images/worm-identify-init.png]]
rlm@449	2740
rlm@449	2741 These action predicates satisfy the recognition requirement of an
rlm@451	2742 empathic recognition system. There is power in the simplicity of
rlm@451	2743 the action predicates. They describe their actions without getting
rlm@531	2744 confused in visual details of the worm. Each one is independent of
rlm@531	2745 position and rotation, but more than that, they are each
rlm@531	2746 independent of irrelevant visual details of the worm and the
rlm@531	2747 environment. They will work regardless of whether the worm is a
rlm@531	2748 different color or heavily textured, or if the environment has
rlm@531	2749 strange lighting.
rlm@531	2750
rlm@531	2751 Consider how the human act of jumping might be described with
rlm@531	2752 body-centered action predicates: You might specify that jumping is
rlm@531	2753 mainly the feeling of your knees bending, your thigh muscles
rlm@531	2754 contracting, and your inner ear experiencing a certain sort of back
rlm@531	2755 and forth acceleration. This representation is a very concrete
rlm@531	2756 description of jumping, couched in terms of muscles and senses, but
rlm@531	2757 it also has the ability to describe almost all kinds of jumping, a
rlm@531	2758 generality that you might think could only be achieved by a very
rlm@531	2759 abstract description. The body centered jumping predicate does not
rlm@531	2760 have terms that consider the color of a person's skin or whether
rlm@531	2761 they are male or female, instead it gets right to the meat of what
rlm@531	2762 jumping actually /is/.
rlm@531	2763
rlm@531	2764 Of course, the action predicates are not directly applicable to
rlm@547	2765 video data, which lacks the advanced sensory information which they
rlm@531	2766 require!
rlm@449	2767
rlm@449	2768 The trick now is to make the action predicates work even when the
rlm@449	2769 sensory data on which they depend is absent. If I can do that, then
rlm@525	2770 I will have gained much.
rlm@435	2771
rlm@531	2772 ** \Phi-space describes the worm's experiences
rlm@449	2773
rlm@449	2774 As a first step towards building empathy, I need to gather all of
rlm@449	2775 the worm's experiences during free play. I use a simple vector to
rlm@449	2776 store all the experiences.
rlm@449	2777
rlm@449	2778 Each element of the experience vector exists in the vast space of
rlm@449	2779 all possible worm-experiences. Most of this vast space is actually
rlm@449	2780 unreachable due to physical constraints of the worm's body. For
rlm@449	2781 example, the worm's segments are connected by hinge joints that put
rlm@451	2782 a practical limit on the worm's range of motions without limiting
rlm@451	2783 its degrees of freedom. Some groupings of senses are impossible;
rlm@451	2784 the worm can not be bent into a circle so that its ends are
rlm@451	2785 touching and at the same time not also experience the sensation of
rlm@451	2786 touching itself.
rlm@449	2787
rlm@451	2788 As the worm moves around during free play and its experience vector
rlm@451	2789 grows larger, the vector begins to define a subspace which is all
rlm@517	2790 the sensations the worm can practically experience during normal
rlm@451	2791 operation. I call this subspace \Phi-space, short for
rlm@451	2792 physical-space. The experience vector defines a path through
rlm@451	2793 \Phi-space. This path has interesting properties that all derive
rlm@533	2794 from physical embodiment. The proprioceptive components of the path
rlm@533	2795 vary smoothly, because in order for the worm to move from one
rlm@451	2796 position to another, it must pass through the intermediate
rlm@533	2797 positions. The path invariably forms loops as common actions are
rlm@533	2798 repeated. Finally and most importantly, proprioception alone
rlm@533	2799 actually gives very strong inference about the other senses. For
rlm@533	2800 example, when the worm is proprioceptively flat over several
rlm@533	2801 frames, you can infer that it is touching the ground and that its
rlm@451	2802 muscles are not active, because if the muscles were active, the
rlm@533	2803 worm would be moving and would not remain perfectly flat. In order
rlm@533	2804 to stay flat, the worm has to be touching the ground, or it would
rlm@451	2805 again be moving out of the flat position due to gravity. If the
rlm@451	2806 worm is positioned in such a way that it interacts with itself,
rlm@451	2807 then it is very likely to be feeling the same tactile feelings as
rlm@451	2808 the last time it was in that position, because it has the same body
rlm@533	2809 as then. As you observe multiple frames of proprioceptive data, you
rlm@533	2810 can become increasingly confident about the exact activations of
rlm@533	2811 the worm's muscles, because it generally takes a unique combination
rlm@533	2812 of muscle contractions to transform the worm's body along a
rlm@533	2813 specific path through \Phi-space.
rlm@533	2814
rlm@533	2815 The worm's total life experience is a long looping path through
rlm@533	2816 \Phi-space. I will now introduce simple way of taking that
rlm@533	2817 experiece path and building a function that can infer complete
rlm@533	2818 sensory experience given only a stream of proprioceptive data. This
rlm@533	2819 /empathy/ function will provide a bridge to use the body centered
rlm@533	2820 action predicates on video-like streams of information.
rlm@533	2821
rlm@535	2822 ** Empathy is the process of building paths in \Phi-space
rlm@449	2823
rlm@450	2824 Here is the core of a basic empathy algorithm, starting with an
rlm@451	2825 experience vector:
rlm@451	2826
rlm@533	2827 An /experience-index/ is an index into the grand experience vector
rlm@534	2828 that defines the worm's life. It is a time-stamp for each set of
rlm@533	2829 sensations the worm has experienced.
rlm@533	2830
rlm@533	2831 First, group the experience-indices into bins according to the
rlm@533	2832 similarity of their proprioceptive data. I organize my bins into a
rlm@534	2833 3 level hierarchy. The smallest bins have an approximate size of
rlm@533	2834 0.001 radians in all proprioceptive dimensions. Each higher level
rlm@533	2835 is 10x bigger than the level below it.
rlm@533	2836
rlm@533	2837 The bins serve as a hashing function for proprioceptive data. Given
rlm@533	2838 a single piece of proprioceptive experience, the bins allow us to
rlm@534	2839 rapidly find all other similar experience-indices of past
rlm@534	2840 experience that had a very similar proprioceptive configuration.
rlm@533	2841 When looking up a proprioceptive experience, if the smallest bin
rlm@534	2842 does not match any previous experience, then successively larger
rlm@533	2843 bins are used until a match is found or we reach the largest bin.
rlm@450	2844
rlm@533	2845 Given a sequence of proprioceptive input, I use the bins to
rlm@534	2846 generate a set of similar experiences for each input using the
rlm@533	2847 tiered proprioceptive bins.
rlm@533	2848
rlm@533	2849 Finally, to infer sensory data, I select the longest consecutive
rlm@534	2850 chain of experiences that threads through the sets of similar
rlm@535	2851 experiences, starting with the current moment as a root and going
rlm@535	2852 backwards. Consecutive experience means that the experiences appear
rlm@535	2853 next to each other in the experience vector.
rlm@535	2854
rlm@535	2855 A stream of proprioceptive input might be:
rlm@533	2856
rlm@535	2857 #+BEGIN_EXAMPLE
rlm@535	2858 [ flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535	2859 #+END_EXAMPLE
rlm@535	2860
rlm@535	2861 The worm's previous experience of lying on the ground and lifting
rlm@547	2862 its head generates possible interpretations for each frame (the
rlm@547	2863 numbers are experience-indices):
rlm@535	2864
rlm@535	2865 #+BEGIN_EXAMPLE
rlm@535	2866 [ flat, flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535	2867 1 1 1 1 1 1 1 4
rlm@535	2868 2 2 2 2 2 2 2
rlm@535	2869 3 3 3 3 3 3 3
rlm@535	2870 7 7 7 7 7 7 7
rlm@535	2871 8 8 8 8 8 8 8
rlm@535	2872 9 9 9 9 9 9 9
rlm@535	2873 #+END_EXAMPLE
rlm@535	2874
rlm@535	2875 These interpretations suggest a new path through phi space:
rlm@535	2876
rlm@535	2877 #+BEGIN_EXAMPLE
rlm@535	2878 [ flat, flat, flat, flat, flat, flat, flat, lift-head ]
rlm@535	2879 6 7 8 9 1 2 3 4
rlm@535	2880 #+END_EXAMPLE
rlm@535	2881
rlm@535	2882 The new path through \Phi-space is synthesized from two actual
rlm@547	2883 paths that the creature has experienced: the "1-2-3-4" chain and
rlm@547	2884 the "6-7-8-9" chain. The "1-2-3-4" chain is necessary because it
rlm@547	2885 ends with the worm lifting its head. It originated from a short
rlm@535	2886 training session where the worm rested on the floor for a brief
rlm@535	2887 while and then raised its head. The "6-7-8-9" chain is part of a
rlm@535	2888 longer chain of inactivity where the worm simply rested on the
rlm@535	2889 floor without moving. It is preferred over a "1-2-3" chain (which
rlm@535	2890 also describes inactivity) because it is longer. The main ideas
rlm@535	2891 again:
rlm@535	2892
rlm@535	2893 - Imagined \Phi-space paths are synthesized by looping and mixing
rlm@535	2894 previous experiences.
rlm@535	2895
rlm@535	2896 - Longer experience paths (less edits) are preferred.
rlm@535	2897
rlm@535	2898 - The present is more important than the past --- more recent
rlm@535	2899 events take precedence in interpretation.
rlm@533	2900
rlm@450	2901 This algorithm has three advantages:
rlm@450	2902
rlm@450	2903 1. It's simple
rlm@450	2904
rlm@451	2905 3. It's very fast -- retrieving possible interpretations takes
rlm@451	2906 constant time. Tracing through chains of interpretations takes
rlm@451	2907 time proportional to the average number of experiences in a
rlm@451	2908 proprioceptive bin. Redundant experiences in \Phi-space can be
rlm@451	2909 merged to save computation.
rlm@450	2910
rlm@450	2911 2. It protects from wrong interpretations of transient ambiguous
rlm@451	2912 proprioceptive data. For example, if the worm is flat for just
rlm@517	2913 an instant, this flatness will not be interpreted as implying
rlm@517	2914 that the worm has its muscles relaxed, since the flatness is
rlm@450	2915 part of a longer chain which includes a distinct pattern of
rlm@451	2916 muscle activation. Markov chains or other memoryless statistical
rlm@451	2917 models that operate on individual frames may very well make this
rlm@451	2918 mistake.
rlm@450	2919
rlm@450	2920 #+caption: Program to convert an experience vector into a
rlm@450	2921 #+caption: proprioceptively binned lookup function.
rlm@450	2922 #+name: bin
rlm@452	2923 #+begin_listing clojure
rlm@450	2924 #+begin_src clojure
rlm@449	2925 (defn bin [digits]
rlm@449	2926 (fn [angles]
rlm@449	2927 (->> angles
rlm@449	2928 (flatten)
rlm@449	2929 (map (juxt #(Math/sin %) #(Math/cos %)))
rlm@449	2930 (flatten)
rlm@449	2931 (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))
rlm@449	2932
rlm@449	2933 (defn gen-phi-scan
rlm@450	2934 "Nearest-neighbors with binning. Only returns a result if
rlm@517	2935 the proprioceptive data is within 10% of a previously recorded
rlm@450	2936 result in all dimensions."
rlm@450	2937 [phi-space]
rlm@449	2938 (let [bin-keys (map bin [3 2 1])
rlm@449	2939 bin-maps
rlm@449	2940 (map (fn [bin-key]
rlm@449	2941 (group-by
rlm@449	2942 (comp bin-key :proprioception phi-space)
rlm@449	2943 (range (count phi-space)))) bin-keys)
rlm@449	2944 lookups (map (fn [bin-key bin-map]
rlm@450	2945 (fn [proprio] (bin-map (bin-key proprio))))
rlm@450	2946 bin-keys bin-maps)]
rlm@449	2947 (fn lookup [proprio-data]
rlm@449	2948 (set (some #(% proprio-data) lookups)))))
rlm@450	2949 #+end_src
rlm@450	2950 #+end_listing
rlm@449	2951
rlm@451	2952 #+caption: =longest-thread= finds the longest path of consecutive
rlm@536	2953 #+caption: past experiences to explain proprioceptive worm data from
rlm@520	2954 #+caption: previous data. Here, the film strip represents the
rlm@531	2955 #+caption: creature's previous experience. Sort sequences of
rlm@520	2956 #+caption: memories are spliced together to match the
rlm@520	2957 #+caption: proprioceptive data. Their carry the other senses
rlm@520	2958 #+caption: along with them.
rlm@451	2959 #+name: phi-space-history-scan
rlm@451	2960 #+ATTR_LaTeX: :width 10cm
rlm@520	2961 [[./images/film-of-imagination.png]]
rlm@451	2962
rlm@451	2963 =longest-thread= infers sensory data by stitching together pieces
rlm@451	2964 from previous experience. It prefers longer chains of previous
rlm@451	2965 experience to shorter ones. For example, during training the worm
rlm@451	2966 might rest on the ground for one second before it performs its
rlm@517	2967 exercises. If during recognition the worm rests on the ground for
rlm@517	2968 five seconds, =longest-thread= will accommodate this five second
rlm@451	2969 rest period by looping the one second rest chain five times.
rlm@451	2970
rlm@517	2971 =longest-thread= takes time proportional to the average number of
rlm@451	2972 entries in a proprioceptive bin, because for each element in the
rlm@517	2973 starting bin it performs a series of set lookups in the preceding
rlm@536	2974 bins. If the total history is limited, then this takes time
rlm@536	2975 proprotional to a only a constant multiple of the number of entries
rlm@536	2976 in the starting bin. This analysis also applies, even if the action
rlm@536	2977 requires multiple longest chains -- it's still the average number
rlm@536	2978 of entries in a proprioceptive bin times the desired chain length.
rlm@536	2979 Because =longest-thread= is so efficient and simple, I can
rlm@536	2980 interpret worm-actions in real time.
rlm@449	2981
rlm@450	2982 #+caption: Program to calculate empathy by tracing though \Phi-space
rlm@450	2983 #+caption: and finding the longest (ie. most coherent) interpretation
rlm@450	2984 #+caption: of the data.
rlm@450	2985 #+name: longest-thread
rlm@452	2986 #+begin_listing clojure
rlm@450	2987 #+begin_src clojure
rlm@449	2988 (defn longest-thread
rlm@449	2989 "Find the longest thread from phi-index-sets. The index sets should
rlm@449	2990 be ordered from most recent to least recent."
rlm@449	2991 [phi-index-sets]
rlm@449	2992 (loop [result '()
rlm@449	2993 [thread-bases & remaining :as phi-index-sets] phi-index-sets]
rlm@449	2994 (if (empty? phi-index-sets)
rlm@449	2995 (vec result)
rlm@449	2996 (let [threads
rlm@449	2997 (for [thread-base thread-bases]
rlm@449	2998 (loop [thread (list thread-base)
rlm@449	2999 remaining remaining]
rlm@449	3000 (let [next-index (dec (first thread))]
rlm@449	3001 (cond (empty? remaining) thread
rlm@449	3002 (contains? (first remaining) next-index)
rlm@449	3003 (recur
rlm@449	3004 (cons next-index thread) (rest remaining))
rlm@449	3005 :else thread))))
rlm@449	3006 longest-thread
rlm@449	3007 (reduce (fn [thread-a thread-b]
rlm@449	3008 (if (> (count thread-a) (count thread-b))
rlm@449	3009 thread-a thread-b))
rlm@449	3010 '(nil)
rlm@449	3011 threads)]
rlm@449	3012 (recur (concat longest-thread result)
rlm@449	3013 (drop (count longest-thread) phi-index-sets))))))
rlm@450	3014 #+end_src
rlm@450	3015 #+end_listing
rlm@450	3016
rlm@451	3017 There is one final piece, which is to replace missing sensory data
rlm@451	3018 with a best-guess estimate. While I could fill in missing data by
rlm@451	3019 using a gradient over the closest known sensory data points,
rlm@451	3020 averages can be misleading. It is certainly possible to create an
rlm@451	3021 impossible sensory state by averaging two possible sensory states.
rlm@536	3022 For example, consider moving your hand in an arc over your head. If
rlm@536	3023 for some reason you only have the initial and final positions of
rlm@536	3024 this movement in your \Phi-space, averaging them together will
rlm@536	3025 produce the proprioceptive sensation of having your hand /inside/
rlm@536	3026 your head, which is physically impossible to ever experience
rlm@536	3027 (barring motor adaption illusions). Therefore I simply replicate
rlm@536	3028 the most recent sensory experience to fill in the gaps.
rlm@449	3029
rlm@449	3030 #+caption: Fill in blanks in sensory experience by replicating the most
rlm@449	3031 #+caption: recent experience.
rlm@449	3032 #+name: infer-nils
rlm@452	3033 #+begin_listing clojure
rlm@449	3034 #+begin_src clojure
rlm@449	3035 (defn infer-nils
rlm@449	3036 "Replace nils with the next available non-nil element in the
rlm@449	3037 sequence, or barring that, 0."
rlm@449	3038 [s]
rlm@449	3039 (loop [i (dec (count s))
rlm@449	3040 v (transient s)]
rlm@449	3041 (if (zero? i) (persistent! v)
rlm@449	3042 (if-let [cur (v i)]
rlm@449	3043 (if (get v (dec i) 0)
rlm@449	3044 (recur (dec i) v)
rlm@449	3045 (recur (dec i) (assoc! v (dec i) cur)))
rlm@449	3046 (recur i (assoc! v i 0))))))
rlm@449	3047 #+end_src
rlm@449	3048 #+end_listing
rlm@435	3049
rlm@541	3050 ** =EMPATH= recognizes actions efficiently
rlm@451	3051
rlm@451	3052 To use =EMPATH= with the worm, I first need to gather a set of
rlm@451	3053 experiences from the worm that includes the actions I want to
rlm@452	3054 recognize. The =generate-phi-space= program (listing
rlm@451	3055 \ref{generate-phi-space} runs the worm through a series of
rlm@545	3056 exercises and gathers those experiences into a vector. The
rlm@451	3057 =do-all-the-things= program is a routine expressed in a simple
rlm@452	3058 muscle contraction script language for automated worm control. It
rlm@452	3059 causes the worm to rest, curl, and wiggle over about 700 frames
rlm@452	3060 (approx. 11 seconds).
rlm@425	3061
rlm@451	3062 #+caption: Program to gather the worm's experiences into a vector for
rlm@451	3063 #+caption: further processing. The =motor-control-program= line uses
rlm@451	3064 #+caption: a motor control script that causes the worm to execute a series
rlm@517	3065 #+caption: of ``exercises'' that include all the action predicates.
rlm@451	3066 #+name: generate-phi-space
rlm@452	3067 #+begin_listing clojure
rlm@451	3068 #+begin_src clojure
rlm@451	3069 (def do-all-the-things
rlm@451	3070 (concat
rlm@451	3071 curl-script
rlm@451	3072 [[300 :d-ex 40]
rlm@451	3073 [320 :d-ex 0]]
rlm@451	3074 (shift-script 280 (take 16 wiggle-script))))
rlm@451	3075
rlm@451	3076 (defn generate-phi-space []
rlm@451	3077 (let [experiences (atom [])]
rlm@451	3078 (run-world
rlm@451	3079 (apply-map
rlm@451	3080 worm-world
rlm@451	3081 (merge
rlm@451	3082 (worm-world-defaults)
rlm@451	3083 {:end-frame 700
rlm@451	3084 :motor-control
rlm@451	3085 (motor-control-program worm-muscle-labels do-all-the-things)
rlm@451	3086 :experiences experiences})))
rlm@451	3087 @experiences))
rlm@451	3088 #+end_src
rlm@451	3089 #+end_listing
rlm@451	3090
rlm@536	3091 #+caption: Use =longest-thread= and a \Phi-space generated from a short
rlm@451	3092 #+caption: exercise routine to interpret actions during free play.
rlm@451	3093 #+name: empathy-debug
rlm@452	3094 #+begin_listing clojure
rlm@451	3095 #+begin_src clojure
rlm@451	3096 (defn init []
rlm@451	3097 (def phi-space (generate-phi-space))
rlm@451	3098 (def phi-scan (gen-phi-scan phi-space)))
rlm@451	3099
rlm@451	3100 (defn empathy-demonstration []
rlm@451	3101 (let [proprio (atom ())]
rlm@451	3102 (fn
rlm@451	3103 [experiences text]
rlm@451	3104 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451	3105 (swap! proprio (partial cons phi-indices))
rlm@451	3106 (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451	3107 empathy (mapv phi-space (infer-nils exp-thread))]
rlm@451	3108 (println-repl (vector:last-n exp-thread 22))
rlm@451	3109 (cond
rlm@451	3110 (grand-circle? empathy) (.setText text "Grand Circle")
rlm@451	3111 (curled? empathy) (.setText text "Curled")
rlm@451	3112 (wiggling? empathy) (.setText text "Wiggling")
rlm@451	3113 (resting? empathy) (.setText text "Resting")
rlm@536	3114 :else (.setText text "Unknown")))))))
rlm@451	3115
rlm@451	3116 (defn empathy-experiment [record]
rlm@451	3117 (.start (worm-world :experience-watch (debug-experience-phi)
rlm@451	3118 :record record :worm worm*)))
rlm@451	3119 #+end_src
rlm@451	3120 #+end_listing
rlm@536	3121
rlm@536	3122 These programs create a test for the empathy system. First, the
rlm@536	3123 worm's \Phi-space is generated from a simple motor script. Then the
rlm@536	3124 worm is re-created in an environment almost exactly identical to
rlm@536	3125 the testing environment for the action-predicates, with one major
rlm@536	3126 difference : the only sensory information available to the system
rlm@536	3127 is proprioception. From just the proprioception data and
rlm@536	3128 \Phi-space, =longest-thread= synthesises a complete record the last
rlm@536	3129 300 sensory experiences of the worm. These synthesized experiences
rlm@536	3130 are fed directly into the action predicates =grand-circle?=,
rlm@536	3131 =curled?=, =wiggling?=, and =resting?= from before and their output
rlm@536	3132 is printed to the screen at each frame.
rlm@451	3133
rlm@451	3134 The result of running =empathy-experiment= is that the system is
rlm@451	3135 generally able to interpret worm actions using the action-predicates
rlm@451	3136 on simulated sensory data just as well as with actual data. Figure
rlm@451	3137 \ref{empathy-debug-image} was generated using =empathy-experiment=:
rlm@451	3138
rlm@451	3139 #+caption: From only proprioceptive data, =EMPATH= was able to infer
rlm@451	3140 #+caption: the complete sensory experience and classify four poses
rlm@517	3141 #+caption: (The last panel shows a composite image of /wiggling/,
rlm@451	3142 #+caption: a dynamic pose.)
rlm@451	3143 #+name: empathy-debug-image
rlm@451	3144 #+ATTR_LaTeX: :width 10cm :placement [H]
rlm@451	3145 [[./images/empathy-1.png]]
rlm@451	3146
rlm@451	3147 One way to measure the performance of =EMPATH= is to compare the
rlm@517	3148 suitability of the imagined sense experience to trigger the same
rlm@451	3149 action predicates as the real sensory experience.
rlm@451	3150
rlm@451	3151 #+caption: Determine how closely empathy approximates actual
rlm@451	3152 #+caption: sensory data.
rlm@451	3153 #+name: test-empathy-accuracy
rlm@452	3154 #+begin_listing clojure
rlm@451	3155 #+begin_src clojure
rlm@451	3156 (def worm-action-label
rlm@451	3157 (juxt grand-circle? curled? wiggling?))
rlm@451	3158
rlm@451	3159 (defn compare-empathy-with-baseline [matches]
rlm@451	3160 (let [proprio (atom ())]
rlm@451	3161 (fn
rlm@451	3162 [experiences text]
rlm@451	3163 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451	3164 (swap! proprio (partial cons phi-indices))
rlm@451	3165 (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451	3166 empathy (mapv phi-space (infer-nils exp-thread))
rlm@451	3167 experience-matches-empathy
rlm@451	3168 (= (worm-action-label experiences)
rlm@451	3169 (worm-action-label empathy))]
rlm@451	3170 (println-repl experience-matches-empathy)
rlm@451	3171 (swap! matches #(conj % experience-matches-empathy)))))))
rlm@451	3172
rlm@451	3173 (defn accuracy [v]
rlm@451	3174 (float (/ (count (filter true? v)) (count v))))
rlm@451	3175
rlm@451	3176 (defn test-empathy-accuracy []
rlm@451	3177 (let [res (atom [])]
rlm@451	3178 (run-world
rlm@451	3179 (worm-world :experience-watch
rlm@451	3180 (compare-empathy-with-baseline res)
rlm@451	3181 :worm worm*))
rlm@451	3182 (accuracy @res)))
rlm@451	3183 #+end_src
rlm@451	3184 #+end_listing
rlm@451	3185
rlm@451	3186 Running =test-empathy-accuracy= using the very short exercise
rlm@451	3187 program defined in listing \ref{generate-phi-space}, and then doing
rlm@517	3188 a similar pattern of activity manually yields an accuracy of around
rlm@451	3189 73%. This is based on very limited worm experience. By training the
rlm@451	3190 worm for longer, the accuracy dramatically improves.
rlm@451	3191
rlm@451	3192 #+caption: Program to generate \Phi-space using manual training.
rlm@451	3193 #+name: manual-phi-space
rlm@451	3194 #+begin_listing clojure
rlm@451	3195 #+begin_src clojure
rlm@451	3196 (defn init-interactive []
rlm@451	3197 (def phi-space
rlm@451	3198 (let [experiences (atom [])]
rlm@451	3199 (run-world
rlm@451	3200 (apply-map
rlm@451	3201 worm-world
rlm@451	3202 (merge
rlm@451	3203 (worm-world-defaults)
rlm@451	3204 {:experiences experiences})))
rlm@451	3205 @experiences))
rlm@451	3206 (def phi-scan (gen-phi-scan phi-space)))
rlm@451	3207 #+end_src
rlm@451	3208 #+end_listing
rlm@451	3209
rlm@451	3210 After about 1 minute of manual training, I was able to achieve 95%
rlm@451	3211 accuracy on manual testing of the worm using =init-interactive= and
rlm@452	3212 =test-empathy-accuracy=. The majority of errors are near the
rlm@452	3213 boundaries of transitioning from one type of action to another.
rlm@452	3214 During these transitions the exact label for the action is more open
rlm@517	3215 to interpretation, and disagreement between empathy and experience
rlm@536	3216 is essentially irrelevant at this point, giving a practical
rlm@536	3217 identification accuracy of even higher than 95%. When I watch this
rlm@536	3218 system myself, I generally see no errors in action identification.
rlm@536	3219
rlm@541	3220 ** Digression: Learning touch sensor layout through free play
rlm@514	3221
rlm@514	3222 In the previous section I showed how to compute actions in terms of
rlm@539	3223 body-centered predicates, but some of those predicates relied on
rlm@539	3224 the average touch activation of pre-defined regions of the worm's
rlm@539	3225 skin. What if, instead of receiving touch pre-grouped into the six
rlm@539	3226 faces of each worm segment, the true topology of the worm's skin
rlm@539	3227 was unknown? This is more similar to how a nerve fiber bundle might
rlm@539	3228 be arranged inside an animal. While two fibers that are close in a
rlm@539	3229 nerve bundle /might/ correspond to two touch sensors that are close
rlm@539	3230 together on the skin, the process of taking a complicated surface
rlm@539	3231 and forcing it into essentially a circle requires that some regions
rlm@539	3232 of skin that are close together in the animal end up far apart in
rlm@539	3233 the nerve bundle.
rlm@452	3234
rlm@452	3235 In this section I show how to automatically learn the skin-topology of
rlm@452	3236 a worm segment by free exploration. As the worm rolls around on the
rlm@452	3237 floor, large sections of its surface get activated. If the worm has
rlm@452	3238 stopped moving, then whatever region of skin that is touching the
rlm@452	3239 floor is probably an important region, and should be recorded.
rlm@452	3240
rlm@452	3241 #+caption: Program to detect whether the worm is in a resting state
rlm@452	3242 #+caption: with one face touching the floor.
rlm@452	3243 #+name: pure-touch
rlm@452	3244 #+begin_listing clojure
rlm@452	3245 #+begin_src clojure
rlm@452	3246 (def full-contact [(float 0.0) (float 0.1)])
rlm@452	3247
rlm@452	3248 (defn pure-touch?
rlm@452	3249 "This is worm specific code to determine if a large region of touch
rlm@452	3250 sensors is either all on or all off."
rlm@452	3251 [[coords touch :as touch-data]]
rlm@452	3252 (= (set (map first touch)) (set full-contact)))
rlm@452	3253 #+end_src
rlm@452	3254 #+end_listing
rlm@452	3255
rlm@452	3256 After collecting these important regions, there will many nearly
rlm@517	3257 similar touch regions. While for some purposes the subtle
rlm@452	3258 differences between these regions will be important, for my
rlm@517	3259 purposes I collapse them into mostly non-overlapping sets using
rlm@517	3260 =remove-similar= in listing \ref{remove-similar}
rlm@517	3261
rlm@517	3262 #+caption: Program to take a list of sets of points and ``collapse them''
rlm@517	3263 #+caption: so that the remaining sets in the list are significantly
rlm@452	3264 #+caption: different from each other. Prefer smaller sets to larger ones.
rlm@517	3265 #+name: remove-similar
rlm@452	3266 #+begin_listing clojure
rlm@452	3267 #+begin_src clojure
rlm@452	3268 (defn remove-similar
rlm@452	3269 [coll]
rlm@452	3270 (loop [result () coll (sort-by (comp - count) coll)]
rlm@452	3271 (if (empty? coll) result
rlm@452	3272 (let [[x & xs] coll
rlm@452	3273 c (count x)]
rlm@452	3274 (if (some
rlm@452	3275 (fn [other-set]
rlm@452	3276 (let [oc (count other-set)]
rlm@452	3277 (< (- (count (union other-set x)) c) (* oc 0.1))))
rlm@452	3278 xs)
rlm@452	3279 (recur result xs)
rlm@452	3280 (recur (cons x result) xs))))))
rlm@452	3281 #+end_src
rlm@452	3282 #+end_listing
rlm@452	3283
rlm@452	3284 Actually running this simulation is easy given =CORTEX='s facilities.
rlm@452	3285
rlm@452	3286 #+caption: Collect experiences while the worm moves around. Filter the touch
rlm@517	3287 #+caption: sensations by stable ones, collapse similar ones together,
rlm@452	3288 #+caption: and report the regions learned.
rlm@452	3289 #+name: learn-touch
rlm@452	3290 #+begin_listing clojure
rlm@452	3291 #+begin_src clojure
rlm@452	3292 (defn learn-touch-regions []
rlm@452	3293 (let [experiences (atom [])
rlm@452	3294 world (apply-map
rlm@452	3295 worm-world
rlm@452	3296 (assoc (worm-segment-defaults)
rlm@452	3297 :experiences experiences))]
rlm@452	3298 (run-world world)
rlm@452	3299 (->>
rlm@452	3300 @experiences
rlm@452	3301 (drop 175)
rlm@452	3302 ;; access the single segment's touch data
rlm@452	3303 (map (comp first :touch))
rlm@452	3304 ;; only deal with "pure" touch data to determine surfaces
rlm@452	3305 (filter pure-touch?)
rlm@452	3306 ;; associate coordinates with touch values
rlm@452	3307 (map (partial apply zipmap))
rlm@452	3308 ;; select those regions where contact is being made
rlm@452	3309 (map (partial group-by second))
rlm@452	3310 (map #(get % full-contact))
rlm@452	3311 (map (partial map first))
rlm@452	3312 ;; remove redundant/subset regions
rlm@452	3313 (map set)
rlm@452	3314 remove-similar)))
rlm@452	3315
rlm@452	3316 (defn learn-and-view-touch-regions []
rlm@452	3317 (map view-touch-region
rlm@452	3318 (learn-touch-regions)))
rlm@452	3319 #+end_src
rlm@452	3320 #+end_listing
rlm@452	3321
rlm@517	3322 The only thing remaining to define is the particular motion the worm
rlm@452	3323 must take. I accomplish this with a simple motor control program.
rlm@452	3324
rlm@452	3325 #+caption: Motor control program for making the worm roll on the ground.
rlm@452	3326 #+caption: This could also be replaced with random motion.
rlm@452	3327 #+name: worm-roll
rlm@452	3328 #+begin_listing clojure
rlm@452	3329 #+begin_src clojure
rlm@452	3330 (defn touch-kinesthetics []
rlm@452	3331 [[170 :lift-1 40]
rlm@452	3332 [190 :lift-1 19]
rlm@452	3333 [206 :lift-1 0]
rlm@452	3334
rlm@452	3335 [400 :lift-2 40]
rlm@452	3336 [410 :lift-2 0]
rlm@452	3337
rlm@452	3338 [570 :lift-2 40]
rlm@452	3339 [590 :lift-2 21]
rlm@452	3340 [606 :lift-2 0]
rlm@452	3341
rlm@452	3342 [800 :lift-1 30]
rlm@452	3343 [809 :lift-1 0]
rlm@452	3344
rlm@452	3345 [900 :roll-2 40]
rlm@452	3346 [905 :roll-2 20]
rlm@452	3347 [910 :roll-2 0]
rlm@452	3348
rlm@452	3349 [1000 :roll-2 40]
rlm@452	3350 [1005 :roll-2 20]
rlm@452	3351 [1010 :roll-2 0]
rlm@452	3352
rlm@452	3353 [1100 :roll-2 40]
rlm@452	3354 [1105 :roll-2 20]
rlm@452	3355 [1110 :roll-2 0]
rlm@452	3356 ])
rlm@452	3357 #+end_src
rlm@452	3358 #+end_listing
rlm@452	3359
rlm@452	3360
rlm@452	3361 #+caption: The small worm rolls around on the floor, driven
rlm@452	3362 #+caption: by the motor control program in listing \ref{worm-roll}.
rlm@452	3363 #+name: worm-roll
rlm@452	3364 #+ATTR_LaTeX: :width 12cm
rlm@452	3365 [[./images/worm-roll.png]]
rlm@452	3366
rlm@452	3367 #+caption: After completing its adventures, the worm now knows
rlm@452	3368 #+caption: how its touch sensors are arranged along its skin. These
rlm@452	3369 #+caption: are the regions that were deemed important by
rlm@537	3370 #+caption: =learn-touch-regions=. Each white square in the rectangles
rlm@537	3371 #+caption: above is a cluster of ``related" touch nodes as determined
rlm@538	3372 #+caption: by the system. Since each square in the ``cross" corresponds
rlm@537	3373 #+caption: to a face, the worm has correctly discovered that it has
rlm@537	3374 #+caption: six faces.
rlm@452	3375 #+name: worm-touch-map
rlm@452	3376 #+ATTR_LaTeX: :width 12cm
rlm@452	3377 [[./images/touch-learn.png]]
rlm@452	3378
rlm@452	3379 While simple, =learn-touch-regions= exploits regularities in both
rlm@452	3380 the worm's physiology and the worm's environment to correctly
rlm@452	3381 deduce that the worm has six sides. Note that =learn-touch-regions=
rlm@452	3382 would work just as well even if the worm's touch sense data were
rlm@517	3383 completely scrambled. The cross shape is just for convenience. This
rlm@452	3384 example justifies the use of pre-defined touch regions in =EMPATH=.
rlm@452	3385
rlm@531	3386 * Contributions
rlm@454	3387
rlm@542	3388 The big idea behind this thesis is a new way to represent and
rlm@542	3389 recognize physical actions -- empathic representation. Actions are
rlm@542	3390 represented as predicates which have available the totality of a
rlm@542	3391 creature's sensory abilities. To recognize the physical actions of
rlm@542	3392 another creature similar to yourself, you imagine what they would
rlm@542	3393 feel by examining the position of their body and relating it to your
rlm@542	3394 own previous experience.
rlm@542	3395
rlm@542	3396 Empathic description of physical actions is very robust and general.
rlm@542	3397 Because the representation is body-centered, it avoids the fragility
rlm@542	3398 of learning from example videos. Because it relies on all of a
rlm@542	3399 creature's senses, it can describe exactly what an action /feels
rlm@542	3400 like/ without getting caught up in irrelevant details such as visual
rlm@542	3401 appearance. I think it is important that a correct description of
rlm@542	3402 jumping (for example) should not waste even a single bit on the
rlm@542	3403 color of a person's clothes or skin; empathic representation can
rlm@542	3404 avoid this waste by describing jumping in terms of touch, muscle
rlm@542	3405 contractions, and the brief feeling of weightlessness. Empathic
rlm@542	3406 representation is very low-level in that it describes actions using
rlm@542	3407 concrete sensory data with little abstraction, but it has the
rlm@542	3408 generality of much more abstract representations!
rlm@542	3409
rlm@542	3410 Another important contribution of this thesis is the development of
rlm@542	3411 the =CORTEX= system, a complete environment for creating simulated
rlm@542	3412 creatures. You have seen how to implement five senses: touch,
rlm@542	3413 proprioception, hearing, vision, and muscle tension. You have seen
rlm@542	3414 how to create new creatures using blender, a 3D modeling tool.
rlm@542	3415
rlm@542	3416 I hope that =CORTEX= will be useful in further research projects. To
rlm@542	3417 this end I have included the full source to =CORTEX= along with a
rlm@542	3418 large suite of tests and examples. I have also created a user guide
rlm@542	3419 for =CORTEX= which is included in an appendix to this thesis.
rlm@447	3420
rlm@461	3421 As a minor digression, you also saw how I used =CORTEX= to enable a
rlm@461	3422 tiny worm to discover the topology of its skin simply by rolling on
rlm@461	3423 the ground.
rlm@461	3424
rlm@461	3425 In conclusion, the main contributions of this thesis are:
rlm@461	3426
rlm@517	3427 - =CORTEX=, a comprehensive platform for embodied AI experiments.
rlm@517	3428 =CORTEX= supports many features lacking in other systems, such
rlm@517	3429 proper simulation of hearing. It is easy to create new =CORTEX=
rlm@517	3430 creatures using Blender, a free 3D modeling program.
rlm@517	3431
rlm@517	3432 - =EMPATH=, which uses =CORTEX= to identify the actions of a
rlm@542	3433 worm-like creature using a computational model of empathy. This
rlm@542	3434 empathic representation of actions is an important new kind of
rlm@542	3435 representation for physical actions.
rlm@517	3436
rlm@488	3437 #+BEGIN_LaTeX
rlm@540	3438 \newpage
rlm@488	3439 \appendix
rlm@488	3440 #+END_LaTeX
rlm@517	3441
rlm@541	3442 * Appendix: =CORTEX= User Guide
rlm@488	3443
rlm@488	3444 Those who write a thesis should endeavor to make their code not only
rlm@517	3445 accessible, but actually usable, as a way to pay back the community
rlm@488	3446 that made the thesis possible in the first place. This thesis would
rlm@488	3447 not be possible without Free Software such as jMonkeyEngine3,
rlm@488	3448 Blender, clojure, emacs, ffmpeg, and many other tools. That is why I
rlm@488	3449 have included this user guide, in the hope that someone else might
rlm@488	3450 find =CORTEX= useful.
rlm@488	3451
rlm@488	3452 ** Obtaining =CORTEX=
rlm@488	3453
rlm@488	3454 You can get cortex from its mercurial repository at
rlm@488	3455 http://hg.bortreb.com/cortex. You may also download =CORTEX=
rlm@488	3456 releases at http://aurellem.org/cortex/releases/. As a condition of
rlm@488	3457 making this thesis, I have also provided Professor Winston the
rlm@488	3458 =CORTEX= source, and he knows how to run the demos and get started.
rlm@488	3459 You may also email me at =cortex@aurellem.org= and I may help where
rlm@488	3460 I can.
rlm@488	3461
rlm@488	3462 ** Running =CORTEX=
rlm@488	3463
rlm@488	3464 =CORTEX= comes with README and INSTALL files that will guide you
rlm@488	3465 through installation and running the test suite. In particular you
rlm@488	3466 should look at test =cortex.test= which contains test suites that
rlm@488	3467 run through all senses and multiple creatures.
rlm@488	3468
rlm@488	3469 ** Creating creatures
rlm@488	3470
rlm@488	3471 Creatures are created using /Blender/, a free 3D modeling program.
rlm@488	3472 You will need Blender version 2.6 when using the =CORTEX= included
rlm@517	3473 in this thesis. You create a =CORTEX= creature in a similar manner
rlm@488	3474 to modeling anything in Blender, except that you also create
rlm@488	3475 several trees of empty nodes which define the creature's senses.
rlm@488	3476
rlm@488	3477 *** Mass
rlm@488	3478
rlm@488	3479 To give an object mass in =CORTEX=, add a ``mass'' metadata label
rlm@488	3480 to the object with the mass in jMonkeyEngine units. Note that
rlm@488	3481 setting the mass to 0 causes the object to be immovable.
rlm@488	3482
rlm@488	3483 *** Joints
rlm@488	3484
rlm@488	3485 Joints are created by creating an empty node named =joints= and
rlm@488	3486 then creating any number of empty child nodes to represent your
rlm@488	3487 creature's joints. The joint will automatically connect the
rlm@488	3488 closest two physical objects. It will help to set the empty node's
rlm@488	3489 display mode to ``Arrows'' so that you can clearly see the
rlm@488	3490 direction of the axes.
rlm@488	3491
rlm@488	3492 Joint nodes should have the following metadata under the ``joint''
rlm@488	3493 label:
rlm@488	3494
rlm@488	3495 #+BEGIN_SRC clojure
rlm@540	3496 ;; ONE of the following, under the label "joint":
rlm@488	3497 {:type :point}
rlm@488	3498
rlm@488	3499 ;; OR
rlm@488	3500
rlm@488	3501 {:type :hinge
rlm@488	3502 :limit [<limit-low> <limit-high>]
rlm@488	3503 :axis (Vector3f. <x> <y> <z>)}
rlm@488	3504 ;;(:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)
rlm@488	3505
rlm@488	3506 ;; OR
rlm@488	3507
rlm@488	3508 {:type :cone
rlm@488	3509 :limit-xz <lim-xz>
rlm@488	3510 :limit-xy <lim-xy>
rlm@488	3511 :twist <lim-twist>} ;(use XZY rotation mode in blender!)
rlm@488	3512 #+END_SRC
rlm@488	3513
rlm@488	3514 *** Eyes
rlm@488	3515
rlm@488	3516 Eyes are created by creating an empty node named =eyes= and then
rlm@488	3517 creating any number of empty child nodes to represent your
rlm@488	3518 creature's eyes.
rlm@488	3519
rlm@488	3520 Eye nodes should have the following metadata under the ``eye''
rlm@488	3521 label:
rlm@488	3522
rlm@488	3523 #+BEGIN_SRC clojure
rlm@488	3524 {:red <red-retina-definition>
rlm@488	3525 :blue <blue-retina-definition>
rlm@488	3526 :green <green-retina-definition>
rlm@488	3527 :all <all-retina-definition>
rlm@488	3528 (<0xrrggbb> <custom-retina-image>)...
rlm@488	3529 }
rlm@488	3530 #+END_SRC
rlm@488	3531
rlm@488	3532 Any of the color channels may be omitted. You may also include
rlm@488	3533 your own color selectors, and in fact :red is equivalent to
rlm@488	3534 0xFF0000 and so forth. The eye will be placed at the same position
rlm@488	3535 as the empty node and will bind to the neatest physical object.
rlm@488	3536 The eye will point outward from the X-axis of the node, and ``up''
rlm@488	3537 will be in the direction of the X-axis of the node. It will help
rlm@488	3538 to set the empty node's display mode to ``Arrows'' so that you can
rlm@488	3539 clearly see the direction of the axes.
rlm@488	3540
rlm@517	3541 Each retina file should contain white pixels wherever you want to be
rlm@488	3542 sensitive to your chosen color. If you want the entire field of
rlm@488	3543 view, specify :all of 0xFFFFFF and a retinal map that is entirely
rlm@488	3544 white.
rlm@488	3545
rlm@488	3546 Here is a sample retinal map:
rlm@488	3547
rlm@488	3548 #+caption: An example retinal profile image. White pixels are
rlm@488	3549 #+caption: photo-sensitive elements. The distribution of white
rlm@488	3550 #+caption: pixels is denser in the middle and falls off at the
rlm@488	3551 #+caption: edges and is inspired by the human retina.
rlm@488	3552 #+name: retina
rlm@488	3553 #+ATTR_LaTeX: :width 7cm :placement [H]
rlm@488	3554 [[./images/retina-small.png]]
rlm@488	3555
rlm@488	3556 *** Hearing
rlm@488	3557
rlm@488	3558 Ears are created by creating an empty node named =ears= and then
rlm@488	3559 creating any number of empty child nodes to represent your
rlm@488	3560 creature's ears.
rlm@488	3561
rlm@488	3562 Ear nodes do not require any metadata.
rlm@488	3563
rlm@488	3564 The ear will bind to and follow the closest physical node.
rlm@488	3565
rlm@488	3566 *** Touch
rlm@488	3567
rlm@488	3568 Touch is handled similarly to mass. To make a particular object
rlm@488	3569 touch sensitive, add metadata of the following form under the
rlm@488	3570 object's ``touch'' metadata field:
rlm@488	3571
rlm@488	3572 #+BEGIN_EXAMPLE
rlm@488	3573 <touch-UV-map-file-name>
rlm@488	3574 #+END_EXAMPLE
rlm@488	3575
rlm@488	3576 You may also include an optional ``scale'' metadata number to
rlm@517	3577 specify the length of the touch feelers. The default is $0.1$,
rlm@488	3578 and this is generally sufficient.
rlm@488	3579
rlm@488	3580 The touch UV should contain white pixels for each touch sensor.
rlm@488	3581
rlm@488	3582 Here is an example touch-uv map that approximates a human finger,
rlm@488	3583 and its corresponding model.
rlm@488	3584
rlm@488	3585 #+caption: This is the tactile-sensor-profile for the upper segment
rlm@488	3586 #+caption: of a fingertip. It defines regions of high touch sensitivity
rlm@488	3587 #+caption: (where there are many white pixels) and regions of low
rlm@488	3588 #+caption: sensitivity (where white pixels are sparse).
rlm@488	3589 #+name: guide-fingertip-UV
rlm@488	3590 #+ATTR_LaTeX: :width 9cm :placement [H]
rlm@488	3591 [[./images/finger-UV.png]]
rlm@488	3592
rlm@488	3593 #+caption: The fingertip UV-image form above applied to a simple
rlm@488	3594 #+caption: model of a fingertip.
rlm@488	3595 #+name: guide-fingertip
rlm@488	3596 #+ATTR_LaTeX: :width 9cm :placement [H]
rlm@488	3597 [[./images/finger-2.png]]
rlm@488	3598
rlm@517	3599 *** Proprioception
rlm@488	3600
rlm@488	3601 Proprioception is tied to each joint node -- nothing special must
rlm@488	3602 be done in a blender model to enable proprioception other than
rlm@488	3603 creating joint nodes.
rlm@488	3604
rlm@488	3605 *** Muscles
rlm@488	3606
rlm@488	3607 Muscles are created by creating an empty node named =muscles= and
rlm@488	3608 then creating any number of empty child nodes to represent your
rlm@488	3609 creature's muscles.
rlm@488	3610
rlm@488	3611
rlm@488	3612 Muscle nodes should have the following metadata under the
rlm@488	3613 ``muscle'' label:
rlm@488	3614
rlm@488	3615 #+BEGIN_EXAMPLE
rlm@488	3616 <muscle-profile-file-name>
rlm@488	3617 #+END_EXAMPLE
rlm@488	3618
rlm@488	3619 Muscles should also have a ``strength'' metadata entry describing
rlm@488	3620 the muscle's total strength at full activation.
rlm@488	3621
rlm@488	3622 Muscle profiles are simple images that contain the relative amount
rlm@488	3623 of muscle power in each simulated alpha motor neuron. The width of
rlm@488	3624 the image is the total size of the motor pool, and the redness of
rlm@488	3625 each neuron is the relative power of that motor pool.
rlm@488	3626
rlm@488	3627 While the profile image can have any dimensions, only the first
rlm@488	3628 line of pixels is used to define the muscle. Here is a sample
rlm@488	3629 muscle profile image that defines a human-like muscle.
rlm@488	3630
rlm@488	3631 #+caption: A muscle profile image that describes the strengths
rlm@488	3632 #+caption: of each motor neuron in a muscle. White is weakest
rlm@488	3633 #+caption: and dark red is strongest. This particular pattern
rlm@488	3634 #+caption: has weaker motor neurons at the beginning, just
rlm@488	3635 #+caption: like human muscle.
rlm@488	3636 #+name: muscle-recruit
rlm@488	3637 #+ATTR_LaTeX: :width 7cm :placement [H]
rlm@488	3638 [[./images/basic-muscle.png]]
rlm@488	3639
rlm@488	3640 Muscles twist the nearest physical object about the muscle node's
rlm@488	3641 Z-axis. I recommend using the ``Single Arrow'' display mode for
rlm@488	3642 muscles and using the right hand rule to determine which way the
rlm@488	3643 muscle will twist. To make a segment that can twist in multiple
rlm@488	3644 directions, create multiple, differently aligned muscles.
rlm@488	3645
rlm@488	3646 ** =CORTEX= API
rlm@488	3647
rlm@488	3648 These are the some functions exposed by =CORTEX= for creating
rlm@488	3649 worlds and simulating creatures. These are in addition to
rlm@488	3650 jMonkeyEngine3's extensive library, which is documented elsewhere.
rlm@488	3651
rlm@488	3652 *** Simulation
rlm@488	3653 - =(world root-node key-map setup-fn update-fn)= :: create
rlm@488	3654 a simulation.
rlm@488	3655 - /root-node/ :: a =com.jme3.scene.Node= object which
rlm@488	3656 contains all of the objects that should be in the
rlm@488	3657 simulation.
rlm@488	3658
rlm@488	3659 - /key-map/ :: a map from strings describing keys to
rlm@488	3660 functions that should be executed whenever that key is
rlm@488	3661 pressed. the functions should take a SimpleApplication
rlm@488	3662 object and a boolean value. The SimpleApplication is the
rlm@488	3663 current simulation that is running, and the boolean is true
rlm@488	3664 if the key is being pressed, and false if it is being
rlm@488	3665 released. As an example,
rlm@488	3666 #+BEGIN_SRC clojure
rlm@488	3667 {"key-j" (fn [game value] (if value (println "key j pressed")))}
rlm@488	3668 #+END_SRC
rlm@488	3669 is a valid key-map which will cause the simulation to print
rlm@488	3670 a message whenever the 'j' key on the keyboard is pressed.
rlm@488	3671
rlm@488	3672 - /setup-fn/ :: a function that takes a =SimpleApplication=
rlm@488	3673 object. It is called once when initializing the simulation.
rlm@488	3674 Use it to create things like lights, change the gravity,
rlm@488	3675 initialize debug nodes, etc.
rlm@488	3676
rlm@488	3677 - /update-fn/ :: this function takes a =SimpleApplication=
rlm@488	3678 object and a float and is called every frame of the
rlm@488	3679 simulation. The float tells how many seconds is has been
rlm@488	3680 since the last frame was rendered, according to whatever
rlm@488	3681 clock jme is currently using. The default is to use IsoTimer
rlm@488	3682 which will result in this value always being the same.
rlm@488	3683
rlm@488	3684 - =(position-camera world position rotation)= :: set the position
rlm@488	3685 of the simulation's main camera.
rlm@488	3686
rlm@488	3687 - =(enable-debug world)= :: turn on debug wireframes for each
rlm@488	3688 simulated object.
rlm@488	3689
rlm@488	3690 - =(set-gravity world gravity)= :: set the gravity of a running
rlm@488	3691 simulation.
rlm@488	3692
rlm@488	3693 - =(box length width height & {options})= :: create a box in the
rlm@488	3694 simulation. Options is a hash map specifying texture, mass,
rlm@488	3695 etc. Possible options are =:name=, =:color=, =:mass=,
rlm@488	3696 =:friction=, =:texture=, =:material=, =:position=,
rlm@488	3697 =:rotation=, =:shape=, and =:physical?=.
rlm@488	3698
rlm@488	3699 - =(sphere radius & {options})= :: create a sphere in the simulation.
rlm@488	3700 Options are the same as in =box=.
rlm@488	3701
rlm@488	3702 - =(load-blender-model file-name)= :: create a node structure
rlm@540	3703 representing the model described in a blender file.
rlm@488	3704
rlm@488	3705 - =(light-up-everything world)= :: distribute a standard compliment
rlm@517	3706 of lights throughout the simulation. Should be adequate for most
rlm@488	3707 purposes.
rlm@488	3708
rlm@517	3709 - =(node-seq node)= :: return a recursive list of the node's
rlm@488	3710 children.
rlm@488	3711
rlm@488	3712 - =(nodify name children)= :: construct a node given a node-name and
rlm@488	3713 desired children.
rlm@488	3714
rlm@488	3715 - =(add-element world element)= :: add an object to a running world
rlm@488	3716 simulation.
rlm@488	3717
rlm@488	3718 - =(set-accuracy world accuracy)= :: change the accuracy of the
rlm@488	3719 world's physics simulator.
rlm@488	3720
rlm@488	3721 - =(asset-manager)= :: get an /AssetManager/, a jMonkeyEngine
rlm@488	3722 construct that is useful for loading textures and is required
rlm@488	3723 for smooth interaction with jMonkeyEngine library functions.
rlm@488	3724
rlm@540	3725 - =(load-bullet)= :: unpack native libraries and initialize the
rlm@540	3726 bullet physics subsystem. This function is required before
rlm@540	3727 other world building functions are called.
rlm@488	3728
rlm@488	3729 *** Creature Manipulation / Import
rlm@488	3730
rlm@488	3731 - =(body! creature)= :: give the creature a physical body.
rlm@488	3732
rlm@488	3733 - =(vision! creature)= :: give the creature a sense of vision.
rlm@488	3734 Returns a list of functions which will each, when called
rlm@488	3735 during a simulation, return the vision data for the channel of
rlm@488	3736 one of the eyes. The functions are ordered depending on the
rlm@488	3737 alphabetical order of the names of the eye nodes in the
rlm@488	3738 blender file. The data returned by the functions is a vector
rlm@488	3739 containing the eye's /topology/, a vector of coordinates, and
rlm@488	3740 the eye's /data/, a vector of RGB values filtered by the eye's
rlm@488	3741 sensitivity.
rlm@488	3742
rlm@488	3743 - =(hearing! creature)= :: give the creature a sense of hearing.
rlm@488	3744 Returns a list of functions, one for each ear, that when
rlm@488	3745 called will return a frame's worth of hearing data for that
rlm@488	3746 ear. The functions are ordered depending on the alphabetical
rlm@488	3747 order of the names of the ear nodes in the blender file. The
rlm@540	3748 data returned by the functions is an array of PCM (pulse code
rlm@540	3749 modulated) wav data.
rlm@488	3750
rlm@488	3751 - =(touch! creature)= :: give the creature a sense of touch. Returns
rlm@488	3752 a single function that must be called with the /root node/ of
rlm@488	3753 the world, and which will return a vector of /touch-data/
rlm@488	3754 one entry for each touch sensitive component, each entry of
rlm@488	3755 which contains a /topology/ that specifies the distribution of
rlm@488	3756 touch sensors, and the /data/, which is a vector of
rlm@488	3757 =[activation, length]= pairs for each touch hair.
rlm@488	3758
rlm@488	3759 - =(proprioception! creature)= :: give the creature the sense of
rlm@488	3760 proprioception. Returns a list of functions, one for each
rlm@488	3761 joint, that when called during a running simulation will
rlm@517	3762 report the =[heading, pitch, roll]= of the joint.
rlm@488	3763
rlm@488	3764 - =(movement! creature)= :: give the creature the power of movement.
rlm@488	3765 Creates a list of functions, one for each muscle, that when
rlm@488	3766 called with an integer, will set the recruitment of that
rlm@488	3767 muscle to that integer, and will report the current power
rlm@488	3768 being exerted by the muscle. Order of muscles is determined by
rlm@488	3769 the alphabetical sort order of the names of the muscle nodes.
rlm@488	3770
rlm@488	3771 *** Visualization/Debug
rlm@488	3772
rlm@488	3773 - =(view-vision)= :: create a function that when called with a list
rlm@488	3774 of visual data returned from the functions made by =vision!=,
rlm@488	3775 will display that visual data on the screen.
rlm@488	3776
rlm@488	3777 - =(view-hearing)= :: same as =view-vision= but for hearing.
rlm@488	3778
rlm@488	3779 - =(view-touch)= :: same as =view-vision= but for touch.
rlm@488	3780
rlm@488	3781 - =(view-proprioception)= :: same as =view-vision= but for
rlm@488	3782 proprioception.
rlm@488	3783
rlm@540	3784 - =(view-movement)= :: same as =view-vision= but for muscles.
rlm@488	3785
rlm@488	3786 - =(view anything)= :: =view= is a polymorphic function that allows
rlm@488	3787 you to inspect almost anything you could reasonably expect to
rlm@488	3788 be able to ``see'' in =CORTEX=.
rlm@488	3789
rlm@488	3790 - =(text anything)= :: =text= is a polymorphic function that allows
rlm@488	3791 you to convert practically anything into a text string.
rlm@488	3792
rlm@488	3793 - =(println-repl anything)= :: print messages to clojure's repl
rlm@488	3794 instead of the simulation's terminal window.
rlm@488	3795
rlm@488	3796 - =(mega-import-jme3)= :: for experimenting at the REPL. This
rlm@488	3797 function will import all jMonkeyEngine3 classes for immediate
rlm@488	3798 use.
rlm@488	3799
rlm@517	3800 - =(display-dilated-time world timer)= :: Shows the time as it is
rlm@488	3801 flowing in the simulation on a HUD display.
rlm@488	3802
rlm@488	3803
rlm@488	3804
rlm@547	3805 TODO -- add a paper about detecting biological motion from only a few dots.

Mercurial > cortex

annotate thesis/cortex.org @ 547:5d89879fc894