annotate thesis/cortex.org @ 475:3ec428e096e5

most of the way to getting touch integrated.
author Robert McIntyre <rlm@mit.edu>
date Fri, 28 Mar 2014 21:48:53 -0400
parents 57c7d5aec8d5
children 5a15611fbb9f
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@470 15 #+end_listing
rlm@465 16
rlm@470 17 #+caption:
rlm@470 18 #+caption:
rlm@470 19 #+caption:
rlm@470 20 #+name: name
rlm@470 21 #+ATTR_LaTeX: :width 10cm
rlm@470 22 [[./images/aurellem-gray.png]]
rlm@470 23
rlm@470 24 #+caption:
rlm@470 25 #+caption:
rlm@470 26 #+caption:
rlm@470 27 #+caption:
rlm@470 28 #+name: name
rlm@470 29 #+begin_listing clojure
rlm@475 30 #+BEGIN_SRC clojure
rlm@475 31 #+END_SRC
rlm@470 32 #+end_listing
rlm@470 33
rlm@470 34 #+caption:
rlm@470 35 #+caption:
rlm@470 36 #+caption:
rlm@470 37 #+name: name
rlm@470 38 #+ATTR_LaTeX: :width 10cm
rlm@470 39 [[./images/aurellem-gray.png]]
rlm@470 40
rlm@465 41
rlm@465 42 * COMMENT Empathy and Embodiment as problem solving strategies
rlm@437 43
rlm@437 44 By the end of this thesis, you will have seen a novel approach to
rlm@437 45 interpreting video using embodiment and empathy. You will have also
rlm@437 46 seen one way to efficiently implement empathy for embodied
rlm@447 47 creatures. Finally, you will become familiar with =CORTEX=, a system
rlm@447 48 for designing and simulating creatures with rich senses, which you
rlm@447 49 may choose to use in your own research.
rlm@437 50
rlm@441 51 This is the core vision of my thesis: That one of the important ways
rlm@441 52 in which we understand others is by imagining ourselves in their
rlm@441 53 position and emphatically feeling experiences relative to our own
rlm@441 54 bodies. By understanding events in terms of our own previous
rlm@441 55 corporeal experience, we greatly constrain the possibilities of what
rlm@441 56 would otherwise be an unwieldy exponential search. This extra
rlm@441 57 constraint can be the difference between easily understanding what
rlm@441 58 is happening in a video and being completely lost in a sea of
rlm@441 59 incomprehensible color and movement.
rlm@435 60
rlm@436 61 ** Recognizing actions in video is extremely difficult
rlm@437 62
rlm@447 63 Consider for example the problem of determining what is happening
rlm@447 64 in a video of which this is one frame:
rlm@437 65
rlm@441 66 #+caption: A cat drinking some water. Identifying this action is
rlm@441 67 #+caption: beyond the state of the art for computers.
rlm@441 68 #+ATTR_LaTeX: :width 7cm
rlm@441 69 [[./images/cat-drinking.jpg]]
rlm@441 70
rlm@441 71 It is currently impossible for any computer program to reliably
rlm@447 72 label such a video as ``drinking''. And rightly so -- it is a very
rlm@441 73 hard problem! What features can you describe in terms of low level
rlm@441 74 functions of pixels that can even begin to describe at a high level
rlm@441 75 what is happening here?
rlm@437 76
rlm@447 77 Or suppose that you are building a program that recognizes chairs.
rlm@448 78 How could you ``see'' the chair in figure \ref{hidden-chair}?
rlm@441 79
rlm@441 80 #+caption: The chair in this image is quite obvious to humans, but I
rlm@448 81 #+caption: doubt that any modern computer vision program can find it.
rlm@441 82 #+name: hidden-chair
rlm@441 83 #+ATTR_LaTeX: :width 10cm
rlm@441 84 [[./images/fat-person-sitting-at-desk.jpg]]
rlm@441 85
rlm@441 86 Finally, how is it that you can easily tell the difference between
rlm@441 87 how the girls /muscles/ are working in figure \ref{girl}?
rlm@441 88
rlm@441 89 #+caption: The mysterious ``common sense'' appears here as you are able
rlm@441 90 #+caption: to discern the difference in how the girl's arm muscles
rlm@441 91 #+caption: are activated between the two images.
rlm@441 92 #+name: girl
rlm@448 93 #+ATTR_LaTeX: :width 7cm
rlm@441 94 [[./images/wall-push.png]]
rlm@437 95
rlm@441 96 Each of these examples tells us something about what might be going
rlm@441 97 on in our minds as we easily solve these recognition problems.
rlm@441 98
rlm@441 99 The hidden chairs show us that we are strongly triggered by cues
rlm@447 100 relating to the position of human bodies, and that we can determine
rlm@447 101 the overall physical configuration of a human body even if much of
rlm@447 102 that body is occluded.
rlm@437 103
rlm@441 104 The picture of the girl pushing against the wall tells us that we
rlm@441 105 have common sense knowledge about the kinetics of our own bodies.
rlm@441 106 We know well how our muscles would have to work to maintain us in
rlm@441 107 most positions, and we can easily project this self-knowledge to
rlm@441 108 imagined positions triggered by images of the human body.
rlm@441 109
rlm@441 110 ** =EMPATH= neatly solves recognition problems
rlm@441 111
rlm@441 112 I propose a system that can express the types of recognition
rlm@441 113 problems above in a form amenable to computation. It is split into
rlm@441 114 four parts:
rlm@441 115
rlm@448 116 - Free/Guided Play :: The creature moves around and experiences the
rlm@448 117 world through its unique perspective. Many otherwise
rlm@448 118 complicated actions are easily described in the language of a
rlm@448 119 full suite of body-centered, rich senses. For example,
rlm@448 120 drinking is the feeling of water sliding down your throat, and
rlm@448 121 cooling your insides. It's often accompanied by bringing your
rlm@448 122 hand close to your face, or bringing your face close to water.
rlm@448 123 Sitting down is the feeling of bending your knees, activating
rlm@448 124 your quadriceps, then feeling a surface with your bottom and
rlm@448 125 relaxing your legs. These body-centered action descriptions
rlm@448 126 can be either learned or hard coded.
rlm@448 127 - Posture Imitation :: When trying to interpret a video or image,
rlm@448 128 the creature takes a model of itself and aligns it with
rlm@448 129 whatever it sees. This alignment can even cross species, as
rlm@448 130 when humans try to align themselves with things like ponies,
rlm@448 131 dogs, or other humans with a different body type.
rlm@448 132 - Empathy :: The alignment triggers associations with
rlm@448 133 sensory data from prior experiences. For example, the
rlm@448 134 alignment itself easily maps to proprioceptive data. Any
rlm@448 135 sounds or obvious skin contact in the video can to a lesser
rlm@448 136 extent trigger previous experience. Segments of previous
rlm@448 137 experiences are stitched together to form a coherent and
rlm@448 138 complete sensory portrait of the scene.
rlm@448 139 - Recognition :: With the scene described in terms of first
rlm@448 140 person sensory events, the creature can now run its
rlm@447 141 action-identification programs on this synthesized sensory
rlm@447 142 data, just as it would if it were actually experiencing the
rlm@447 143 scene first-hand. If previous experience has been accurately
rlm@447 144 retrieved, and if it is analogous enough to the scene, then
rlm@447 145 the creature will correctly identify the action in the scene.
rlm@447 146
rlm@441 147 For example, I think humans are able to label the cat video as
rlm@447 148 ``drinking'' because they imagine /themselves/ as the cat, and
rlm@441 149 imagine putting their face up against a stream of water and
rlm@441 150 sticking out their tongue. In that imagined world, they can feel
rlm@441 151 the cool water hitting their tongue, and feel the water entering
rlm@447 152 their body, and are able to recognize that /feeling/ as drinking.
rlm@447 153 So, the label of the action is not really in the pixels of the
rlm@447 154 image, but is found clearly in a simulation inspired by those
rlm@447 155 pixels. An imaginative system, having been trained on drinking and
rlm@447 156 non-drinking examples and learning that the most important
rlm@447 157 component of drinking is the feeling of water sliding down one's
rlm@447 158 throat, would analyze a video of a cat drinking in the following
rlm@447 159 manner:
rlm@441 160
rlm@447 161 1. Create a physical model of the video by putting a ``fuzzy''
rlm@447 162 model of its own body in place of the cat. Possibly also create
rlm@447 163 a simulation of the stream of water.
rlm@441 164
rlm@441 165 2. Play out this simulated scene and generate imagined sensory
rlm@441 166 experience. This will include relevant muscle contractions, a
rlm@441 167 close up view of the stream from the cat's perspective, and most
rlm@441 168 importantly, the imagined feeling of water entering the
rlm@443 169 mouth. The imagined sensory experience can come from a
rlm@441 170 simulation of the event, but can also be pattern-matched from
rlm@441 171 previous, similar embodied experience.
rlm@441 172
rlm@441 173 3. The action is now easily identified as drinking by the sense of
rlm@441 174 taste alone. The other senses (such as the tongue moving in and
rlm@441 175 out) help to give plausibility to the simulated action. Note that
rlm@441 176 the sense of vision, while critical in creating the simulation,
rlm@441 177 is not critical for identifying the action from the simulation.
rlm@441 178
rlm@441 179 For the chair examples, the process is even easier:
rlm@441 180
rlm@441 181 1. Align a model of your body to the person in the image.
rlm@441 182
rlm@441 183 2. Generate proprioceptive sensory data from this alignment.
rlm@437 184
rlm@441 185 3. Use the imagined proprioceptive data as a key to lookup related
rlm@441 186 sensory experience associated with that particular proproceptive
rlm@441 187 feeling.
rlm@437 188
rlm@443 189 4. Retrieve the feeling of your bottom resting on a surface, your
rlm@443 190 knees bent, and your leg muscles relaxed.
rlm@437 191
rlm@441 192 5. This sensory information is consistent with the =sitting?=
rlm@441 193 sensory predicate, so you (and the entity in the image) must be
rlm@441 194 sitting.
rlm@440 195
rlm@441 196 6. There must be a chair-like object since you are sitting.
rlm@440 197
rlm@441 198 Empathy offers yet another alternative to the age-old AI
rlm@441 199 representation question: ``What is a chair?'' --- A chair is the
rlm@441 200 feeling of sitting.
rlm@441 201
rlm@441 202 My program, =EMPATH= uses this empathic problem solving technique
rlm@441 203 to interpret the actions of a simple, worm-like creature.
rlm@437 204
rlm@441 205 #+caption: The worm performs many actions during free play such as
rlm@441 206 #+caption: curling, wiggling, and resting.
rlm@441 207 #+name: worm-intro
rlm@446 208 #+ATTR_LaTeX: :width 15cm
rlm@445 209 [[./images/worm-intro-white.png]]
rlm@437 210
rlm@462 211 #+caption: =EMPATH= recognized and classified each of these
rlm@462 212 #+caption: poses by inferring the complete sensory experience
rlm@462 213 #+caption: from proprioceptive data.
rlm@441 214 #+name: worm-recognition-intro
rlm@446 215 #+ATTR_LaTeX: :width 15cm
rlm@445 216 [[./images/worm-poses.png]]
rlm@441 217
rlm@441 218 One powerful advantage of empathic problem solving is that it
rlm@441 219 factors the action recognition problem into two easier problems. To
rlm@441 220 use empathy, you need an /aligner/, which takes the video and a
rlm@441 221 model of your body, and aligns the model with the video. Then, you
rlm@441 222 need a /recognizer/, which uses the aligned model to interpret the
rlm@441 223 action. The power in this method lies in the fact that you describe
rlm@448 224 all actions form a body-centered viewpoint. You are less tied to
rlm@447 225 the particulars of any visual representation of the actions. If you
rlm@441 226 teach the system what ``running'' is, and you have a good enough
rlm@441 227 aligner, the system will from then on be able to recognize running
rlm@441 228 from any point of view, even strange points of view like above or
rlm@441 229 underneath the runner. This is in contrast to action recognition
rlm@448 230 schemes that try to identify actions using a non-embodied approach.
rlm@448 231 If these systems learn about running as viewed from the side, they
rlm@448 232 will not automatically be able to recognize running from any other
rlm@448 233 viewpoint.
rlm@441 234
rlm@441 235 Another powerful advantage is that using the language of multiple
rlm@441 236 body-centered rich senses to describe body-centerd actions offers a
rlm@441 237 massive boost in descriptive capability. Consider how difficult it
rlm@441 238 would be to compose a set of HOG filters to describe the action of
rlm@447 239 a simple worm-creature ``curling'' so that its head touches its
rlm@447 240 tail, and then behold the simplicity of describing thus action in a
rlm@441 241 language designed for the task (listing \ref{grand-circle-intro}):
rlm@441 242
rlm@446 243 #+caption: Body-centerd actions are best expressed in a body-centered
rlm@446 244 #+caption: language. This code detects when the worm has curled into a
rlm@446 245 #+caption: full circle. Imagine how you would replicate this functionality
rlm@446 246 #+caption: using low-level pixel features such as HOG filters!
rlm@446 247 #+name: grand-circle-intro
rlm@452 248 #+attr_latex: [htpb]
rlm@452 249 #+begin_listing clojure
rlm@446 250 #+begin_src clojure
rlm@446 251 (defn grand-circle?
rlm@446 252 "Does the worm form a majestic circle (one end touching the other)?"
rlm@446 253 [experiences]
rlm@446 254 (and (curled? experiences)
rlm@446 255 (let [worm-touch (:touch (peek experiences))
rlm@446 256 tail-touch (worm-touch 0)
rlm@446 257 head-touch (worm-touch 4)]
rlm@462 258 (and (< 0.2 (contact worm-segment-bottom-tip tail-touch))
rlm@462 259 (< 0.2 (contact worm-segment-top-tip head-touch))))))
rlm@446 260 #+end_src
rlm@446 261 #+end_listing
rlm@446 262
rlm@435 263
rlm@449 264 ** =CORTEX= is a toolkit for building sensate creatures
rlm@435 265
rlm@448 266 I built =CORTEX= to be a general AI research platform for doing
rlm@448 267 experiments involving multiple rich senses and a wide variety and
rlm@448 268 number of creatures. I intend it to be useful as a library for many
rlm@462 269 more projects than just this thesis. =CORTEX= was necessary to meet
rlm@462 270 a need among AI researchers at CSAIL and beyond, which is that
rlm@462 271 people often will invent neat ideas that are best expressed in the
rlm@448 272 language of creatures and senses, but in order to explore those
rlm@448 273 ideas they must first build a platform in which they can create
rlm@448 274 simulated creatures with rich senses! There are many ideas that
rlm@448 275 would be simple to execute (such as =EMPATH=), but attached to them
rlm@448 276 is the multi-month effort to make a good creature simulator. Often,
rlm@448 277 that initial investment of time proves to be too much, and the
rlm@448 278 project must make do with a lesser environment.
rlm@435 279
rlm@448 280 =CORTEX= is well suited as an environment for embodied AI research
rlm@448 281 for three reasons:
rlm@448 282
rlm@448 283 - You can create new creatures using Blender, a popular 3D modeling
rlm@448 284 program. Each sense can be specified using special blender nodes
rlm@448 285 with biologically inspired paramaters. You need not write any
rlm@448 286 code to create a creature, and can use a wide library of
rlm@448 287 pre-existing blender models as a base for your own creatures.
rlm@448 288
rlm@448 289 - =CORTEX= implements a wide variety of senses, including touch,
rlm@448 290 proprioception, vision, hearing, and muscle tension. Complicated
rlm@448 291 senses like touch, and vision involve multiple sensory elements
rlm@448 292 embedded in a 2D surface. You have complete control over the
rlm@448 293 distribution of these sensor elements through the use of simple
rlm@448 294 png image files. In particular, =CORTEX= implements more
rlm@448 295 comprehensive hearing than any other creature simulation system
rlm@448 296 available.
rlm@448 297
rlm@448 298 - =CORTEX= supports any number of creatures and any number of
rlm@448 299 senses. Time in =CORTEX= dialates so that the simulated creatures
rlm@448 300 always precieve a perfectly smooth flow of time, regardless of
rlm@448 301 the actual computational load.
rlm@448 302
rlm@448 303 =CORTEX= is built on top of =jMonkeyEngine3=, which is a video game
rlm@448 304 engine designed to create cross-platform 3D desktop games. =CORTEX=
rlm@448 305 is mainly written in clojure, a dialect of =LISP= that runs on the
rlm@448 306 java virtual machine (JVM). The API for creating and simulating
rlm@449 307 creatures and senses is entirely expressed in clojure, though many
rlm@449 308 senses are implemented at the layer of jMonkeyEngine or below. For
rlm@449 309 example, for the sense of hearing I use a layer of clojure code on
rlm@449 310 top of a layer of java JNI bindings that drive a layer of =C++=
rlm@449 311 code which implements a modified version of =OpenAL= to support
rlm@449 312 multiple listeners. =CORTEX= is the only simulation environment
rlm@449 313 that I know of that can support multiple entities that can each
rlm@449 314 hear the world from their own perspective. Other senses also
rlm@449 315 require a small layer of Java code. =CORTEX= also uses =bullet=, a
rlm@449 316 physics simulator written in =C=.
rlm@448 317
rlm@448 318 #+caption: Here is the worm from above modeled in Blender, a free
rlm@448 319 #+caption: 3D-modeling program. Senses and joints are described
rlm@448 320 #+caption: using special nodes in Blender.
rlm@448 321 #+name: worm-recognition-intro
rlm@448 322 #+ATTR_LaTeX: :width 12cm
rlm@448 323 [[./images/blender-worm.png]]
rlm@448 324
rlm@449 325 Here are some thing I anticipate that =CORTEX= might be used for:
rlm@449 326
rlm@449 327 - exploring new ideas about sensory integration
rlm@449 328 - distributed communication among swarm creatures
rlm@449 329 - self-learning using free exploration,
rlm@449 330 - evolutionary algorithms involving creature construction
rlm@449 331 - exploration of exoitic senses and effectors that are not possible
rlm@449 332 in the real world (such as telekenisis or a semantic sense)
rlm@449 333 - imagination using subworlds
rlm@449 334
rlm@451 335 During one test with =CORTEX=, I created 3,000 creatures each with
rlm@448 336 their own independent senses and ran them all at only 1/80 real
rlm@448 337 time. In another test, I created a detailed model of my own hand,
rlm@448 338 equipped with a realistic distribution of touch (more sensitive at
rlm@448 339 the fingertips), as well as eyes and ears, and it ran at around 1/4
rlm@451 340 real time.
rlm@448 341
rlm@451 342 #+BEGIN_LaTeX
rlm@449 343 \begin{sidewaysfigure}
rlm@449 344 \includegraphics[width=9.5in]{images/full-hand.png}
rlm@451 345 \caption{
rlm@451 346 I modeled my own right hand in Blender and rigged it with all the
rlm@451 347 senses that {\tt CORTEX} supports. My simulated hand has a
rlm@451 348 biologically inspired distribution of touch sensors. The senses are
rlm@451 349 displayed on the right, and the simulation is displayed on the
rlm@451 350 left. Notice that my hand is curling its fingers, that it can see
rlm@451 351 its own finger from the eye in its palm, and that it can feel its
rlm@451 352 own thumb touching its palm.}
rlm@449 353 \end{sidewaysfigure}
rlm@451 354 #+END_LaTeX
rlm@448 355
rlm@437 356 ** Contributions
rlm@435 357
rlm@451 358 - I built =CORTEX=, a comprehensive platform for embodied AI
rlm@451 359 experiments. =CORTEX= supports many features lacking in other
rlm@451 360 systems, such proper simulation of hearing. It is easy to create
rlm@451 361 new =CORTEX= creatures using Blender, a free 3D modeling program.
rlm@449 362
rlm@451 363 - I built =EMPATH=, which uses =CORTEX= to identify the actions of
rlm@451 364 a worm-like creature using a computational model of empathy.
rlm@449 365
rlm@436 366 * Building =CORTEX=
rlm@435 367
rlm@462 368 I intend for =CORTEX= to be used as a general purpose library for
rlm@462 369 building creatures and outfitting them with senses, so that it will
rlm@462 370 be useful for other researchers who want to test out ideas of their
rlm@462 371 own. To this end, wherver I have had to make archetictural choices
rlm@462 372 about =CORTEX=, I have chosen to give as much freedom to the user as
rlm@462 373 possible, so that =CORTEX= may be used for things I have not
rlm@462 374 forseen.
rlm@462 375
rlm@465 376 ** COMMENT Simulation or Reality?
rlm@462 377
rlm@462 378 The most important archetictural decision of all is the choice to
rlm@462 379 use a computer-simulated environemnt in the first place! The world
rlm@462 380 is a vast and rich place, and for now simulations are a very poor
rlm@462 381 reflection of its complexity. It may be that there is a significant
rlm@462 382 qualatative difference between dealing with senses in the real
rlm@468 383 world and dealing with pale facilimilies of them in a simulation.
rlm@468 384 What are the advantages and disadvantages of a simulation vs.
rlm@468 385 reality?
rlm@462 386
rlm@462 387 *** Simulation
rlm@462 388
rlm@462 389 The advantages of virtual reality are that when everything is a
rlm@462 390 simulation, experiments in that simulation are absolutely
rlm@462 391 reproducible. It's also easier to change the character and world
rlm@462 392 to explore new situations and different sensory combinations.
rlm@462 393
rlm@462 394 If the world is to be simulated on a computer, then not only do
rlm@462 395 you have to worry about whether the character's senses are rich
rlm@462 396 enough to learn from the world, but whether the world itself is
rlm@462 397 rendered with enough detail and realism to give enough working
rlm@462 398 material to the character's senses. To name just a few
rlm@462 399 difficulties facing modern physics simulators: destructibility of
rlm@462 400 the environment, simulation of water/other fluids, large areas,
rlm@462 401 nonrigid bodies, lots of objects, smoke. I don't know of any
rlm@462 402 computer simulation that would allow a character to take a rock
rlm@462 403 and grind it into fine dust, then use that dust to make a clay
rlm@462 404 sculpture, at least not without spending years calculating the
rlm@462 405 interactions of every single small grain of dust. Maybe a
rlm@462 406 simulated world with today's limitations doesn't provide enough
rlm@462 407 richness for real intelligence to evolve.
rlm@462 408
rlm@462 409 *** Reality
rlm@462 410
rlm@462 411 The other approach for playing with senses is to hook your
rlm@462 412 software up to real cameras, microphones, robots, etc., and let it
rlm@462 413 loose in the real world. This has the advantage of eliminating
rlm@462 414 concerns about simulating the world at the expense of increasing
rlm@462 415 the complexity of implementing the senses. Instead of just
rlm@462 416 grabbing the current rendered frame for processing, you have to
rlm@462 417 use an actual camera with real lenses and interact with photons to
rlm@462 418 get an image. It is much harder to change the character, which is
rlm@462 419 now partly a physical robot of some sort, since doing so involves
rlm@462 420 changing things around in the real world instead of modifying
rlm@462 421 lines of code. While the real world is very rich and definitely
rlm@462 422 provides enough stimulation for intelligence to develop as
rlm@462 423 evidenced by our own existence, it is also uncontrollable in the
rlm@462 424 sense that a particular situation cannot be recreated perfectly or
rlm@462 425 saved for later use. It is harder to conduct science because it is
rlm@462 426 harder to repeat an experiment. The worst thing about using the
rlm@462 427 real world instead of a simulation is the matter of time. Instead
rlm@462 428 of simulated time you get the constant and unstoppable flow of
rlm@462 429 real time. This severely limits the sorts of software you can use
rlm@462 430 to program the AI because all sense inputs must be handled in real
rlm@462 431 time. Complicated ideas may have to be implemented in hardware or
rlm@462 432 may simply be impossible given the current speed of our
rlm@462 433 processors. Contrast this with a simulation, in which the flow of
rlm@462 434 time in the simulated world can be slowed down to accommodate the
rlm@462 435 limitations of the character's programming. In terms of cost,
rlm@462 436 doing everything in software is far cheaper than building custom
rlm@462 437 real-time hardware. All you need is a laptop and some patience.
rlm@435 438
rlm@465 439 ** COMMENT Because of Time, simulation is perferable to reality
rlm@435 440
rlm@462 441 I envision =CORTEX= being used to support rapid prototyping and
rlm@462 442 iteration of ideas. Even if I could put together a well constructed
rlm@462 443 kit for creating robots, it would still not be enough because of
rlm@462 444 the scourge of real-time processing. Anyone who wants to test their
rlm@462 445 ideas in the real world must always worry about getting their
rlm@465 446 algorithms to run fast enough to process information in real time.
rlm@465 447 The need for real time processing only increases if multiple senses
rlm@465 448 are involved. In the extreme case, even simple algorithms will have
rlm@465 449 to be accelerated by ASIC chips or FPGAs, turning what would
rlm@465 450 otherwise be a few lines of code and a 10x speed penality into a
rlm@465 451 multi-month ordeal. For this reason, =CORTEX= supports
rlm@462 452 /time-dialiation/, which scales back the framerate of the
rlm@465 453 simulation in proportion to the amount of processing each frame.
rlm@465 454 From the perspective of the creatures inside the simulation, time
rlm@465 455 always appears to flow at a constant rate, regardless of how
rlm@462 456 complicated the envorimnent becomes or how many creatures are in
rlm@462 457 the simulation. The cost is that =CORTEX= can sometimes run slower
rlm@462 458 than real time. This can also be an advantage, however ---
rlm@462 459 simulations of very simple creatures in =CORTEX= generally run at
rlm@462 460 40x on my machine!
rlm@462 461
rlm@469 462 ** COMMENT What is a sense?
rlm@468 463
rlm@468 464 If =CORTEX= is to support a wide variety of senses, it would help
rlm@468 465 to have a better understanding of what a ``sense'' actually is!
rlm@468 466 While vision, touch, and hearing all seem like they are quite
rlm@468 467 different things, I was supprised to learn during the course of
rlm@468 468 this thesis that they (and all physical senses) can be expressed as
rlm@468 469 exactly the same mathematical object due to a dimensional argument!
rlm@468 470
rlm@468 471 Human beings are three-dimensional objects, and the nerves that
rlm@468 472 transmit data from our various sense organs to our brain are
rlm@468 473 essentially one-dimensional. This leaves up to two dimensions in
rlm@468 474 which our sensory information may flow. For example, imagine your
rlm@468 475 skin: it is a two-dimensional surface around a three-dimensional
rlm@468 476 object (your body). It has discrete touch sensors embedded at
rlm@468 477 various points, and the density of these sensors corresponds to the
rlm@468 478 sensitivity of that region of skin. Each touch sensor connects to a
rlm@468 479 nerve, all of which eventually are bundled together as they travel
rlm@468 480 up the spinal cord to the brain. Intersect the spinal nerves with a
rlm@468 481 guillotining plane and you will see all of the sensory data of the
rlm@468 482 skin revealed in a roughly circular two-dimensional image which is
rlm@468 483 the cross section of the spinal cord. Points on this image that are
rlm@468 484 close together in this circle represent touch sensors that are
rlm@468 485 /probably/ close together on the skin, although there is of course
rlm@468 486 some cutting and rearrangement that has to be done to transfer the
rlm@468 487 complicated surface of the skin onto a two dimensional image.
rlm@468 488
rlm@468 489 Most human senses consist of many discrete sensors of various
rlm@468 490 properties distributed along a surface at various densities. For
rlm@468 491 skin, it is Pacinian corpuscles, Meissner's corpuscles, Merkel's
rlm@468 492 disks, and Ruffini's endings, which detect pressure and vibration
rlm@468 493 of various intensities. For ears, it is the stereocilia distributed
rlm@468 494 along the basilar membrane inside the cochlea; each one is
rlm@468 495 sensitive to a slightly different frequency of sound. For eyes, it
rlm@468 496 is rods and cones distributed along the surface of the retina. In
rlm@468 497 each case, we can describe the sense with a surface and a
rlm@468 498 distribution of sensors along that surface.
rlm@468 499
rlm@468 500 The neat idea is that every human sense can be effectively
rlm@468 501 described in terms of a surface containing embedded sensors. If the
rlm@468 502 sense had any more dimensions, then there wouldn't be enough room
rlm@468 503 in the spinal chord to transmit the information!
rlm@468 504
rlm@468 505 Therefore, =CORTEX= must support the ability to create objects and
rlm@468 506 then be able to ``paint'' points along their surfaces to describe
rlm@468 507 each sense.
rlm@468 508
rlm@468 509 Fortunately this idea is already a well known computer graphics
rlm@468 510 technique called called /UV-mapping/. The three-dimensional surface
rlm@468 511 of a model is cut and smooshed until it fits on a two-dimensional
rlm@468 512 image. You paint whatever you want on that image, and when the
rlm@468 513 three-dimensional shape is rendered in a game the smooshing and
rlm@468 514 cutting is reversed and the image appears on the three-dimensional
rlm@468 515 object.
rlm@468 516
rlm@468 517 To make a sense, interpret the UV-image as describing the
rlm@468 518 distribution of that senses sensors. To get different types of
rlm@468 519 sensors, you can either use a different color for each type of
rlm@468 520 sensor, or use multiple UV-maps, each labeled with that sensor
rlm@468 521 type. I generally use a white pixel to mean the presence of a
rlm@468 522 sensor and a black pixel to mean the absence of a sensor, and use
rlm@468 523 one UV-map for each sensor-type within a given sense.
rlm@468 524
rlm@468 525 #+CAPTION: The UV-map for an elongated icososphere. The white
rlm@468 526 #+caption: dots each represent a touch sensor. They are dense
rlm@468 527 #+caption: in the regions that describe the tip of the finger,
rlm@468 528 #+caption: and less dense along the dorsal side of the finger
rlm@468 529 #+caption: opposite the tip.
rlm@468 530 #+name: finger-UV
rlm@468 531 #+ATTR_latex: :width 10cm
rlm@468 532 [[./images/finger-UV.png]]
rlm@468 533
rlm@468 534 #+caption: Ventral side of the UV-mapped finger. Notice the
rlm@468 535 #+caption: density of touch sensors at the tip.
rlm@468 536 #+name: finger-side-view
rlm@468 537 #+ATTR_LaTeX: :width 10cm
rlm@468 538 [[./images/finger-1.png]]
rlm@468 539
rlm@465 540 ** COMMENT Video game engines are a great starting point
rlm@462 541
rlm@462 542 I did not need to write my own physics simulation code or shader to
rlm@462 543 build =CORTEX=. Doing so would lead to a system that is impossible
rlm@462 544 for anyone but myself to use anyway. Instead, I use a video game
rlm@462 545 engine as a base and modify it to accomodate the additional needs
rlm@462 546 of =CORTEX=. Video game engines are an ideal starting point to
rlm@462 547 build =CORTEX=, because they are not far from being creature
rlm@463 548 building systems themselves.
rlm@462 549
rlm@462 550 First off, general purpose video game engines come with a physics
rlm@462 551 engine and lighting / sound system. The physics system provides
rlm@462 552 tools that can be co-opted to serve as touch, proprioception, and
rlm@462 553 muscles. Since some games support split screen views, a good video
rlm@462 554 game engine will allow you to efficiently create multiple cameras
rlm@463 555 in the simulated world that can be used as eyes. Video game systems
rlm@463 556 offer integrated asset management for things like textures and
rlm@468 557 creatures models, providing an avenue for defining creatures. They
rlm@468 558 also understand UV-mapping, since this technique is used to apply a
rlm@468 559 texture to a model. Finally, because video game engines support a
rlm@468 560 large number of users, as long as =CORTEX= doesn't stray too far
rlm@468 561 from the base system, other researchers can turn to this community
rlm@468 562 for help when doing their research.
rlm@463 563
rlm@465 564 ** COMMENT =CORTEX= is based on jMonkeyEngine3
rlm@463 565
rlm@463 566 While preparing to build =CORTEX= I studied several video game
rlm@463 567 engines to see which would best serve as a base. The top contenders
rlm@463 568 were:
rlm@463 569
rlm@463 570 - [[http://www.idsoftware.com][Quake II]]/[[http://www.bytonic.de/html/jake2.html][Jake2]] :: The Quake II engine was designed by ID
rlm@463 571 software in 1997. All the source code was released by ID
rlm@463 572 software into the Public Domain several years ago, and as a
rlm@463 573 result it has been ported to many different languages. This
rlm@463 574 engine was famous for its advanced use of realistic shading
rlm@463 575 and had decent and fast physics simulation. The main advantage
rlm@463 576 of the Quake II engine is its simplicity, but I ultimately
rlm@463 577 rejected it because the engine is too tied to the concept of a
rlm@463 578 first-person shooter game. One of the problems I had was that
rlm@463 579 there does not seem to be any easy way to attach multiple
rlm@463 580 cameras to a single character. There are also several physics
rlm@463 581 clipping issues that are corrected in a way that only applies
rlm@463 582 to the main character and do not apply to arbitrary objects.
rlm@463 583
rlm@463 584 - [[http://source.valvesoftware.com/][Source Engine]] :: The Source Engine evolved from the Quake II
rlm@463 585 and Quake I engines and is used by Valve in the Half-Life
rlm@463 586 series of games. The physics simulation in the Source Engine
rlm@463 587 is quite accurate and probably the best out of all the engines
rlm@463 588 I investigated. There is also an extensive community actively
rlm@463 589 working with the engine. However, applications that use the
rlm@463 590 Source Engine must be written in C++, the code is not open, it
rlm@463 591 only runs on Windows, and the tools that come with the SDK to
rlm@463 592 handle models and textures are complicated and awkward to use.
rlm@463 593
rlm@463 594 - [[http://jmonkeyengine.com/][jMonkeyEngine3]] :: jMonkeyEngine3 is a new library for creating
rlm@463 595 games in Java. It uses OpenGL to render to the screen and uses
rlm@463 596 screengraphs to avoid drawing things that do not appear on the
rlm@463 597 screen. It has an active community and several games in the
rlm@463 598 pipeline. The engine was not built to serve any particular
rlm@463 599 game but is instead meant to be used for any 3D game.
rlm@463 600
rlm@463 601 I chose jMonkeyEngine3 because it because it had the most features
rlm@464 602 out of all the free projects I looked at, and because I could then
rlm@463 603 write my code in clojure, an implementation of =LISP= that runs on
rlm@463 604 the JVM.
rlm@435 605
rlm@469 606 ** COMMENT =CORTEX= uses Blender to create creature models
rlm@435 607
rlm@464 608 For the simple worm-like creatures I will use later on in this
rlm@464 609 thesis, I could define a simple API in =CORTEX= that would allow
rlm@464 610 one to create boxes, spheres, etc., and leave that API as the sole
rlm@464 611 way to create creatures. However, for =CORTEX= to truly be useful
rlm@468 612 for other projects, it needs a way to construct complicated
rlm@464 613 creatures. If possible, it would be nice to leverage work that has
rlm@464 614 already been done by the community of 3D modelers, or at least
rlm@464 615 enable people who are talented at moedling but not programming to
rlm@468 616 design =CORTEX= creatures.
rlm@464 617
rlm@464 618 Therefore, I use Blender, a free 3D modeling program, as the main
rlm@464 619 way to create creatures in =CORTEX=. However, the creatures modeled
rlm@464 620 in Blender must also be simple to simulate in jMonkeyEngine3's game
rlm@468 621 engine, and must also be easy to rig with =CORTEX='s senses. I
rlm@468 622 accomplish this with extensive use of Blender's ``empty nodes.''
rlm@464 623
rlm@468 624 Empty nodes have no mass, physical presence, or appearance, but
rlm@468 625 they can hold metadata and have names. I use a tree structure of
rlm@468 626 empty nodes to specify senses in the following manner:
rlm@468 627
rlm@468 628 - Create a single top-level empty node whose name is the name of
rlm@468 629 the sense.
rlm@468 630 - Add empty nodes which each contain meta-data relevant to the
rlm@468 631 sense, including a UV-map describing the number/distribution of
rlm@468 632 sensors if applicable.
rlm@468 633 - Make each empty-node the child of the top-level node.
rlm@468 634
rlm@468 635 #+caption: An example of annoting a creature model with empty
rlm@468 636 #+caption: nodes to describe the layout of senses. There are
rlm@468 637 #+caption: multiple empty nodes which each describe the position
rlm@468 638 #+caption: of muscles, ears, eyes, or joints.
rlm@468 639 #+name: sense-nodes
rlm@468 640 #+ATTR_LaTeX: :width 10cm
rlm@468 641 [[./images/empty-sense-nodes.png]]
rlm@468 642
rlm@469 643 ** COMMENT Bodies are composed of segments connected by joints
rlm@468 644
rlm@468 645 Blender is a general purpose animation tool, which has been used in
rlm@468 646 the past to create high quality movies such as Sintel
rlm@468 647 \cite{sintel}. Though Blender can model and render even complicated
rlm@468 648 things like water, it is crucual to keep models that are meant to
rlm@468 649 be simulated as creatures simple. =Bullet=, which =CORTEX= uses
rlm@468 650 though jMonkeyEngine3, is a rigid-body physics system. This offers
rlm@468 651 a compromise between the expressiveness of a game level and the
rlm@468 652 speed at which it can be simulated, and it means that creatures
rlm@468 653 should be naturally expressed as rigid components held together by
rlm@468 654 joint constraints.
rlm@468 655
rlm@468 656 But humans are more like a squishy bag with wrapped around some
rlm@468 657 hard bones which define the overall shape. When we move, our skin
rlm@468 658 bends and stretches to accomodate the new positions of our bones.
rlm@468 659
rlm@468 660 One way to make bodies composed of rigid pieces connected by joints
rlm@468 661 /seem/ more human-like is to use an /armature/, (or /rigging/)
rlm@468 662 system, which defines a overall ``body mesh'' and defines how the
rlm@468 663 mesh deforms as a function of the position of each ``bone'' which
rlm@468 664 is a standard rigid body. This technique is used extensively to
rlm@468 665 model humans and create realistic animations. It is not a good
rlm@468 666 technique for physical simulation, however because it creates a lie
rlm@468 667 -- the skin is not a physical part of the simulation and does not
rlm@468 668 interact with any objects in the world or itself. Objects will pass
rlm@468 669 right though the skin until they come in contact with the
rlm@468 670 underlying bone, which is a physical object. Whithout simulating
rlm@468 671 the skin, the sense of touch has little meaning, and the creature's
rlm@468 672 own vision will lie to it about the true extent of its body.
rlm@468 673 Simulating the skin as a physical object requires some way to
rlm@468 674 continuously update the physical model of the skin along with the
rlm@468 675 movement of the bones, which is unacceptably slow compared to rigid
rlm@468 676 body simulation.
rlm@468 677
rlm@468 678 Therefore, instead of using the human-like ``deformable bag of
rlm@468 679 bones'' approach, I decided to base my body plans on multiple solid
rlm@468 680 objects that are connected by joints, inspired by the robot =EVE=
rlm@468 681 from the movie WALL-E.
rlm@464 682
rlm@464 683 #+caption: =EVE= from the movie WALL-E. This body plan turns
rlm@464 684 #+caption: out to be much better suited to my purposes than a more
rlm@464 685 #+caption: human-like one.
rlm@465 686 #+ATTR_LaTeX: :width 10cm
rlm@464 687 [[./images/Eve.jpg]]
rlm@464 688
rlm@464 689 =EVE='s body is composed of several rigid components that are held
rlm@464 690 together by invisible joint constraints. This is what I mean by
rlm@464 691 ``eve-like''. The main reason that I use eve-style bodies is for
rlm@464 692 efficiency, and so that there will be correspondence between the
rlm@468 693 AI's semses and the physical presence of its body. Each individual
rlm@464 694 section is simulated by a separate rigid body that corresponds
rlm@464 695 exactly with its visual representation and does not change.
rlm@464 696 Sections are connected by invisible joints that are well supported
rlm@464 697 in jMonkeyEngine3. Bullet, the physics backend for jMonkeyEngine3,
rlm@464 698 can efficiently simulate hundreds of rigid bodies connected by
rlm@468 699 joints. Just because sections are rigid does not mean they have to
rlm@468 700 stay as one piece forever; they can be dynamically replaced with
rlm@468 701 multiple sections to simulate splitting in two. This could be used
rlm@468 702 to simulate retractable claws or =EVE='s hands, which are able to
rlm@468 703 coalesce into one object in the movie.
rlm@465 704
rlm@469 705 *** Solidifying/Connecting a body
rlm@465 706
rlm@469 707 =CORTEX= creates a creature in two steps: first, it traverses the
rlm@469 708 nodes in the blender file and creates physical representations for
rlm@469 709 any of them that have mass defined in their blender meta-data.
rlm@466 710
rlm@466 711 #+caption: Program for iterating through the nodes in a blender file
rlm@466 712 #+caption: and generating physical jMonkeyEngine3 objects with mass
rlm@466 713 #+caption: and a matching physics shape.
rlm@466 714 #+name: name
rlm@466 715 #+begin_listing clojure
rlm@466 716 #+begin_src clojure
rlm@466 717 (defn physical!
rlm@466 718 "Iterate through the nodes in creature and make them real physical
rlm@466 719 objects in the simulation."
rlm@466 720 [#^Node creature]
rlm@466 721 (dorun
rlm@466 722 (map
rlm@466 723 (fn [geom]
rlm@466 724 (let [physics-control
rlm@466 725 (RigidBodyControl.
rlm@466 726 (HullCollisionShape.
rlm@466 727 (.getMesh geom))
rlm@466 728 (if-let [mass (meta-data geom "mass")]
rlm@466 729 (float mass) (float 1)))]
rlm@466 730 (.addControl geom physics-control)))
rlm@466 731 (filter #(isa? (class %) Geometry )
rlm@466 732 (node-seq creature)))))
rlm@466 733 #+end_src
rlm@466 734 #+end_listing
rlm@465 735
rlm@469 736 The next step to making a proper body is to connect those pieces
rlm@469 737 together with joints. jMonkeyEngine has a large array of joints
rlm@469 738 available via =bullet=, such as Point2Point, Cone, Hinge, and a
rlm@469 739 generic Six Degree of Freedom joint, with or without spring
rlm@469 740 restitution.
rlm@465 741
rlm@469 742 Joints are treated a lot like proper senses, in that there is a
rlm@469 743 top-level empty node named ``joints'' whose children each
rlm@469 744 represent a joint.
rlm@466 745
rlm@469 746 #+caption: View of the hand model in Blender showing the main ``joints''
rlm@469 747 #+caption: node (highlighted in yellow) and its children which each
rlm@469 748 #+caption: represent a joint in the hand. Each joint node has metadata
rlm@469 749 #+caption: specifying what sort of joint it is.
rlm@469 750 #+name: blender-hand
rlm@469 751 #+ATTR_LaTeX: :width 10cm
rlm@469 752 [[./images/hand-screenshot1.png]]
rlm@469 753
rlm@469 754
rlm@469 755 =CORTEX='s procedure for binding the creature together with joints
rlm@469 756 is as follows:
rlm@469 757
rlm@469 758 - Find the children of the ``joints'' node.
rlm@469 759 - Determine the two spatials the joint is meant to connect.
rlm@469 760 - Create the joint based on the meta-data of the empty node.
rlm@469 761
rlm@469 762 The higher order function =sense-nodes= from =cortex.sense=
rlm@469 763 simplifies finding the joints based on their parent ``joints''
rlm@469 764 node.
rlm@466 765
rlm@466 766 #+caption: Retrieving the children empty nodes from a single
rlm@466 767 #+caption: named empty node is a common pattern in =CORTEX=
rlm@466 768 #+caption: further instances of this technique for the senses
rlm@466 769 #+caption: will be omitted
rlm@466 770 #+name: get-empty-nodes
rlm@466 771 #+begin_listing clojure
rlm@466 772 #+begin_src clojure
rlm@466 773 (defn sense-nodes
rlm@466 774 "For some senses there is a special empty blender node whose
rlm@466 775 children are considered markers for an instance of that sense. This
rlm@466 776 function generates functions to find those children, given the name
rlm@466 777 of the special parent node."
rlm@466 778 [parent-name]
rlm@466 779 (fn [#^Node creature]
rlm@466 780 (if-let [sense-node (.getChild creature parent-name)]
rlm@466 781 (seq (.getChildren sense-node)) [])))
rlm@466 782
rlm@466 783 (def
rlm@466 784 ^{:doc "Return the children of the creature's \"joints\" node."
rlm@466 785 :arglists '([creature])}
rlm@466 786 joints
rlm@466 787 (sense-nodes "joints"))
rlm@466 788 #+end_src
rlm@466 789 #+end_listing
rlm@466 790
rlm@469 791 To find a joint's targets, =CORTEX= creates a small cube, centered
rlm@469 792 around the empty-node, and grows the cube exponentially until it
rlm@469 793 intersects two physical objects. The objects are ordered according
rlm@469 794 to the joint's rotation, with the first one being the object that
rlm@469 795 has more negative coordinates in the joint's reference frame.
rlm@469 796 Since the objects must be physical, the empty-node itself escapes
rlm@469 797 detection. Because the objects must be physical, =joint-targets=
rlm@469 798 must be called /after/ =physical!= is called.
rlm@464 799
rlm@469 800 #+caption: Program to find the targets of a joint node by
rlm@469 801 #+caption: exponentiallly growth of a search cube.
rlm@469 802 #+name: joint-targets
rlm@469 803 #+begin_listing clojure
rlm@469 804 #+begin_src clojure
rlm@466 805 (defn joint-targets
rlm@466 806 "Return the two closest two objects to the joint object, ordered
rlm@466 807 from bottom to top according to the joint's rotation."
rlm@466 808 [#^Node parts #^Node joint]
rlm@466 809 (loop [radius (float 0.01)]
rlm@466 810 (let [results (CollisionResults.)]
rlm@466 811 (.collideWith
rlm@466 812 parts
rlm@466 813 (BoundingBox. (.getWorldTranslation joint)
rlm@466 814 radius radius radius) results)
rlm@466 815 (let [targets
rlm@466 816 (distinct
rlm@466 817 (map #(.getGeometry %) results))]
rlm@466 818 (if (>= (count targets) 2)
rlm@466 819 (sort-by
rlm@466 820 #(let [joint-ref-frame-position
rlm@466 821 (jme-to-blender
rlm@466 822 (.mult
rlm@466 823 (.inverse (.getWorldRotation joint))
rlm@466 824 (.subtract (.getWorldTranslation %)
rlm@466 825 (.getWorldTranslation joint))))]
rlm@466 826 (.dot (Vector3f. 1 1 1) joint-ref-frame-position))
rlm@466 827 (take 2 targets))
rlm@466 828 (recur (float (* radius 2))))))))
rlm@469 829 #+end_src
rlm@469 830 #+end_listing
rlm@464 831
rlm@469 832 Once =CORTEX= finds all joints and targets, it creates them using
rlm@469 833 a dispatch on the metadata of each joint node.
rlm@466 834
rlm@469 835 #+caption: Program to dispatch on blender metadata and create joints
rlm@469 836 #+caption: sutiable for physical simulation.
rlm@469 837 #+name: joint-dispatch
rlm@469 838 #+begin_listing clojure
rlm@469 839 #+begin_src clojure
rlm@466 840 (defmulti joint-dispatch
rlm@466 841 "Translate blender pseudo-joints into real JME joints."
rlm@466 842 (fn [constraints & _]
rlm@466 843 (:type constraints)))
rlm@466 844
rlm@466 845 (defmethod joint-dispatch :point
rlm@466 846 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466 847 (doto (SixDofJoint. control-a control-b pivot-a pivot-b false)
rlm@466 848 (.setLinearLowerLimit Vector3f/ZERO)
rlm@466 849 (.setLinearUpperLimit Vector3f/ZERO)))
rlm@466 850
rlm@466 851 (defmethod joint-dispatch :hinge
rlm@466 852 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466 853 (let [axis (if-let [axis (:axis constraints)] axis Vector3f/UNIT_X)
rlm@466 854 [limit-1 limit-2] (:limit constraints)
rlm@466 855 hinge-axis (.mult rotation (blender-to-jme axis))]
rlm@466 856 (doto (HingeJoint. control-a control-b pivot-a pivot-b
rlm@466 857 hinge-axis hinge-axis)
rlm@466 858 (.setLimit limit-1 limit-2))))
rlm@466 859
rlm@466 860 (defmethod joint-dispatch :cone
rlm@466 861 [constraints control-a control-b pivot-a pivot-b rotation]
rlm@466 862 (let [limit-xz (:limit-xz constraints)
rlm@466 863 limit-xy (:limit-xy constraints)
rlm@466 864 twist (:twist constraints)]
rlm@466 865 (doto (ConeJoint. control-a control-b pivot-a pivot-b
rlm@466 866 rotation rotation)
rlm@466 867 (.setLimit (float limit-xz) (float limit-xy)
rlm@466 868 (float twist)))))
rlm@469 869 #+end_src
rlm@469 870 #+end_listing
rlm@466 871
rlm@469 872 All that is left for joints it to combine the above pieces into a
rlm@469 873 something that can operate on the collection of nodes that a
rlm@469 874 blender file represents.
rlm@466 875
rlm@469 876 #+caption: Program to completely create a joint given information
rlm@469 877 #+caption: from a blender file.
rlm@469 878 #+name: connect
rlm@469 879 #+begin_listing clojure
rlm@466 880 #+begin_src clojure
rlm@466 881 (defn connect
rlm@466 882 "Create a joint between 'obj-a and 'obj-b at the location of
rlm@466 883 'joint. The type of joint is determined by the metadata on 'joint.
rlm@466 884
rlm@466 885 Here are some examples:
rlm@466 886 {:type :point}
rlm@466 887 {:type :hinge :limit [0 (/ Math/PI 2)] :axis (Vector3f. 0 1 0)}
rlm@466 888 (:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)
rlm@466 889
rlm@466 890 {:type :cone :limit-xz 0]
rlm@466 891 :limit-xy 0]
rlm@466 892 :twist 0]} (use XZY rotation mode in blender!)"
rlm@466 893 [#^Node obj-a #^Node obj-b #^Node joint]
rlm@466 894 (let [control-a (.getControl obj-a RigidBodyControl)
rlm@466 895 control-b (.getControl obj-b RigidBodyControl)
rlm@466 896 joint-center (.getWorldTranslation joint)
rlm@466 897 joint-rotation (.toRotationMatrix (.getWorldRotation joint))
rlm@466 898 pivot-a (world-to-local obj-a joint-center)
rlm@466 899 pivot-b (world-to-local obj-b joint-center)]
rlm@466 900 (if-let
rlm@466 901 [constraints (map-vals eval (read-string (meta-data joint "joint")))]
rlm@466 902 ;; A side-effect of creating a joint registers
rlm@466 903 ;; it with both physics objects which in turn
rlm@466 904 ;; will register the joint with the physics system
rlm@466 905 ;; when the simulation is started.
rlm@466 906 (joint-dispatch constraints
rlm@466 907 control-a control-b
rlm@466 908 pivot-a pivot-b
rlm@466 909 joint-rotation))))
rlm@469 910 #+end_src
rlm@469 911 #+end_listing
rlm@466 912
rlm@469 913 In general, whenever =CORTEX= exposes a sense (or in this case
rlm@469 914 physicality), it provides a function of the type =sense!=, which
rlm@469 915 takes in a collection of nodes and augments it to support that
rlm@469 916 sense. The function returns any controlls necessary to use that
rlm@469 917 sense. In this case =body!= cerates a physical body and returns no
rlm@469 918 control functions.
rlm@466 919
rlm@469 920 #+caption: Program to give joints to a creature.
rlm@469 921 #+name: name
rlm@469 922 #+begin_listing clojure
rlm@469 923 #+begin_src clojure
rlm@466 924 (defn joints!
rlm@466 925 "Connect the solid parts of the creature with physical joints. The
rlm@466 926 joints are taken from the \"joints\" node in the creature."
rlm@466 927 [#^Node creature]
rlm@466 928 (dorun
rlm@466 929 (map
rlm@466 930 (fn [joint]
rlm@466 931 (let [[obj-a obj-b] (joint-targets creature joint)]
rlm@466 932 (connect obj-a obj-b joint)))
rlm@466 933 (joints creature))))
rlm@466 934 (defn body!
rlm@466 935 "Endow the creature with a physical body connected with joints. The
rlm@466 936 particulars of the joints and the masses of each body part are
rlm@466 937 determined in blender."
rlm@466 938 [#^Node creature]
rlm@466 939 (physical! creature)
rlm@466 940 (joints! creature))
rlm@469 941 #+end_src
rlm@469 942 #+end_listing
rlm@466 943
rlm@469 944 All of the code you have just seen amounts to only 130 lines, yet
rlm@469 945 because it builds on top of Blender and jMonkeyEngine3, those few
rlm@469 946 lines pack quite a punch!
rlm@466 947
rlm@469 948 The hand from figure \ref{blender-hand}, which was modeled after
rlm@469 949 my own right hand, can now be given joints and simulated as a
rlm@469 950 creature.
rlm@466 951
rlm@469 952 #+caption: With the ability to create physical creatures from blender,
rlm@469 953 #+caption: =CORTEX= gets one step closer to becomming a full creature
rlm@469 954 #+caption: simulation environment.
rlm@469 955 #+name: name
rlm@469 956 #+ATTR_LaTeX: :width 15cm
rlm@469 957 [[./images/physical-hand.png]]
rlm@468 958
rlm@472 959 ** COMMENT Eyes reuse standard video game components
rlm@436 960
rlm@470 961 Vision is one of the most important senses for humans, so I need to
rlm@470 962 build a simulated sense of vision for my AI. I will do this with
rlm@470 963 simulated eyes. Each eye can be independently moved and should see
rlm@470 964 its own version of the world depending on where it is.
rlm@470 965
rlm@470 966 Making these simulated eyes a reality is simple because
rlm@470 967 jMonkeyEngine already contains extensive support for multiple views
rlm@470 968 of the same 3D simulated world. The reason jMonkeyEngine has this
rlm@470 969 support is because the support is necessary to create games with
rlm@470 970 split-screen views. Multiple views are also used to create
rlm@470 971 efficient pseudo-reflections by rendering the scene from a certain
rlm@470 972 perspective and then projecting it back onto a surface in the 3D
rlm@470 973 world.
rlm@470 974
rlm@470 975 #+caption: jMonkeyEngine supports multiple views to enable
rlm@470 976 #+caption: split-screen games, like GoldenEye, which was one of
rlm@470 977 #+caption: the first games to use split-screen views.
rlm@470 978 #+name: name
rlm@470 979 #+ATTR_LaTeX: :width 10cm
rlm@470 980 [[./images/goldeneye-4-player.png]]
rlm@470 981
rlm@470 982 *** A Brief Description of jMonkeyEngine's Rendering Pipeline
rlm@470 983
rlm@470 984 jMonkeyEngine allows you to create a =ViewPort=, which represents a
rlm@470 985 view of the simulated world. You can create as many of these as you
rlm@470 986 want. Every frame, the =RenderManager= iterates through each
rlm@470 987 =ViewPort=, rendering the scene in the GPU. For each =ViewPort= there
rlm@470 988 is a =FrameBuffer= which represents the rendered image in the GPU.
rlm@470 989
rlm@470 990 #+caption: =ViewPorts= are cameras in the world. During each frame,
rlm@470 991 #+caption: the =RenderManager= records a snapshot of what each view
rlm@470 992 #+caption: is currently seeing; these snapshots are =FrameBuffer= objects.
rlm@470 993 #+name: name
rlm@470 994 #+ATTR_LaTeX: :width 10cm
rlm@470 995 [[../images/diagram_rendermanager2.png]]
rlm@470 996
rlm@470 997 Each =ViewPort= can have any number of attached =SceneProcessor=
rlm@470 998 objects, which are called every time a new frame is rendered. A
rlm@470 999 =SceneProcessor= receives its =ViewPort's= =FrameBuffer= and can do
rlm@470 1000 whatever it wants to the data. Often this consists of invoking GPU
rlm@470 1001 specific operations on the rendered image. The =SceneProcessor= can
rlm@470 1002 also copy the GPU image data to RAM and process it with the CPU.
rlm@470 1003
rlm@470 1004 *** Appropriating Views for Vision
rlm@470 1005
rlm@470 1006 Each eye in the simulated creature needs its own =ViewPort= so
rlm@470 1007 that it can see the world from its own perspective. To this
rlm@470 1008 =ViewPort=, I add a =SceneProcessor= that feeds the visual data to
rlm@470 1009 any arbitrary continuation function for further processing. That
rlm@470 1010 continuation function may perform both CPU and GPU operations on
rlm@470 1011 the data. To make this easy for the continuation function, the
rlm@470 1012 =SceneProcessor= maintains appropriately sized buffers in RAM to
rlm@470 1013 hold the data. It does not do any copying from the GPU to the CPU
rlm@470 1014 itself because it is a slow operation.
rlm@470 1015
rlm@470 1016 #+caption: Function to make the rendered secne in jMonkeyEngine
rlm@470 1017 #+caption: available for further processing.
rlm@470 1018 #+name: pipeline-1
rlm@470 1019 #+begin_listing clojure
rlm@470 1020 #+begin_src clojure
rlm@470 1021 (defn vision-pipeline
rlm@470 1022 "Create a SceneProcessor object which wraps a vision processing
rlm@470 1023 continuation function. The continuation is a function that takes
rlm@470 1024 [#^Renderer r #^FrameBuffer fb #^ByteBuffer b #^BufferedImage bi],
rlm@470 1025 each of which has already been appropriately sized."
rlm@470 1026 [continuation]
rlm@470 1027 (let [byte-buffer (atom nil)
rlm@470 1028 renderer (atom nil)
rlm@470 1029 image (atom nil)]
rlm@470 1030 (proxy [SceneProcessor] []
rlm@470 1031 (initialize
rlm@470 1032 [renderManager viewPort]
rlm@470 1033 (let [cam (.getCamera viewPort)
rlm@470 1034 width (.getWidth cam)
rlm@470 1035 height (.getHeight cam)]
rlm@470 1036 (reset! renderer (.getRenderer renderManager))
rlm@470 1037 (reset! byte-buffer
rlm@470 1038 (BufferUtils/createByteBuffer
rlm@470 1039 (* width height 4)))
rlm@470 1040 (reset! image (BufferedImage.
rlm@470 1041 width height
rlm@470 1042 BufferedImage/TYPE_4BYTE_ABGR))))
rlm@470 1043 (isInitialized [] (not (nil? @byte-buffer)))
rlm@470 1044 (reshape [_ _ _])
rlm@470 1045 (preFrame [_])
rlm@470 1046 (postQueue [_])
rlm@470 1047 (postFrame
rlm@470 1048 [#^FrameBuffer fb]
rlm@470 1049 (.clear @byte-buffer)
rlm@470 1050 (continuation @renderer fb @byte-buffer @image))
rlm@470 1051 (cleanup []))))
rlm@470 1052 #+end_src
rlm@470 1053 #+end_listing
rlm@470 1054
rlm@470 1055 The continuation function given to =vision-pipeline= above will be
rlm@470 1056 given a =Renderer= and three containers for image data. The
rlm@470 1057 =FrameBuffer= references the GPU image data, but the pixel data
rlm@470 1058 can not be used directly on the CPU. The =ByteBuffer= and
rlm@470 1059 =BufferedImage= are initially "empty" but are sized to hold the
rlm@470 1060 data in the =FrameBuffer=. I call transferring the GPU image data
rlm@470 1061 to the CPU structures "mixing" the image data.
rlm@470 1062
rlm@470 1063 *** Optical sensor arrays are described with images and referenced with metadata
rlm@470 1064
rlm@470 1065 The vision pipeline described above handles the flow of rendered
rlm@470 1066 images. Now, =CORTEX= needs simulated eyes to serve as the source
rlm@470 1067 of these images.
rlm@470 1068
rlm@470 1069 An eye is described in blender in the same way as a joint. They
rlm@470 1070 are zero dimensional empty objects with no geometry whose local
rlm@470 1071 coordinate system determines the orientation of the resulting eye.
rlm@470 1072 All eyes are children of a parent node named "eyes" just as all
rlm@470 1073 joints have a parent named "joints". An eye binds to the nearest
rlm@470 1074 physical object with =bind-sense=.
rlm@470 1075
rlm@470 1076 #+caption: Here, the camera is created based on metadata on the
rlm@470 1077 #+caption: eye-node and attached to the nearest physical object
rlm@470 1078 #+caption: with =bind-sense=
rlm@470 1079 #+name: add-eye
rlm@470 1080 #+begin_listing clojure
rlm@470 1081 (defn add-eye!
rlm@470 1082 "Create a Camera centered on the current position of 'eye which
rlm@470 1083 follows the closest physical node in 'creature. The camera will
rlm@470 1084 point in the X direction and use the Z vector as up as determined
rlm@470 1085 by the rotation of these vectors in blender coordinate space. Use
rlm@470 1086 XZY rotation for the node in blender."
rlm@470 1087 [#^Node creature #^Spatial eye]
rlm@470 1088 (let [target (closest-node creature eye)
rlm@470 1089 [cam-width cam-height]
rlm@470 1090 ;;[640 480] ;; graphics card on laptop doesn't support
rlm@470 1091 ;; arbitray dimensions.
rlm@470 1092 (eye-dimensions eye)
rlm@470 1093 cam (Camera. cam-width cam-height)
rlm@470 1094 rot (.getWorldRotation eye)]
rlm@470 1095 (.setLocation cam (.getWorldTranslation eye))
rlm@470 1096 (.lookAtDirection
rlm@470 1097 cam ; this part is not a mistake and
rlm@470 1098 (.mult rot Vector3f/UNIT_X) ; is consistent with using Z in
rlm@470 1099 (.mult rot Vector3f/UNIT_Y)) ; blender as the UP vector.
rlm@470 1100 (.setFrustumPerspective
rlm@470 1101 cam (float 45)
rlm@470 1102 (float (/ (.getWidth cam) (.getHeight cam)))
rlm@470 1103 (float 1)
rlm@470 1104 (float 1000))
rlm@470 1105 (bind-sense target cam) cam))
rlm@470 1106 #+end_listing
rlm@470 1107
rlm@470 1108 *** Simulated Retina
rlm@470 1109
rlm@470 1110 An eye is a surface (the retina) which contains many discrete
rlm@470 1111 sensors to detect light. These sensors can have different
rlm@470 1112 light-sensing properties. In humans, each discrete sensor is
rlm@470 1113 sensitive to red, blue, green, or gray. These different types of
rlm@470 1114 sensors can have different spatial distributions along the retina.
rlm@470 1115 In humans, there is a fovea in the center of the retina which has
rlm@470 1116 a very high density of color sensors, and a blind spot which has
rlm@470 1117 no sensors at all. Sensor density decreases in proportion to
rlm@470 1118 distance from the fovea.
rlm@470 1119
rlm@470 1120 I want to be able to model any retinal configuration, so my
rlm@470 1121 eye-nodes in blender contain metadata pointing to images that
rlm@470 1122 describe the precise position of the individual sensors using
rlm@470 1123 white pixels. The meta-data also describes the precise sensitivity
rlm@470 1124 to light that the sensors described in the image have. An eye can
rlm@470 1125 contain any number of these images. For example, the metadata for
rlm@470 1126 an eye might look like this:
rlm@470 1127
rlm@470 1128 #+begin_src clojure
rlm@470 1129 {0xFF0000 "Models/test-creature/retina-small.png"}
rlm@470 1130 #+end_src
rlm@470 1131
rlm@470 1132 #+caption: An example retinal profile image. White pixels are
rlm@470 1133 #+caption: photo-sensitive elements. The distribution of white
rlm@470 1134 #+caption: pixels is denser in the middle and falls off at the
rlm@470 1135 #+caption: edges and is inspired by the human retina.
rlm@470 1136 #+name: retina
rlm@470 1137 #+ATTR_LaTeX: :width 10cm
rlm@470 1138 [[./images/retina-small.png]]
rlm@470 1139
rlm@470 1140 Together, the number 0xFF0000 and the image image above describe
rlm@470 1141 the placement of red-sensitive sensory elements.
rlm@470 1142
rlm@470 1143 Meta-data to very crudely approximate a human eye might be
rlm@470 1144 something like this:
rlm@470 1145
rlm@470 1146 #+begin_src clojure
rlm@470 1147 (let [retinal-profile "Models/test-creature/retina-small.png"]
rlm@470 1148 {0xFF0000 retinal-profile
rlm@470 1149 0x00FF00 retinal-profile
rlm@470 1150 0x0000FF retinal-profile
rlm@470 1151 0xFFFFFF retinal-profile})
rlm@470 1152 #+end_src
rlm@470 1153
rlm@470 1154 The numbers that serve as keys in the map determine a sensor's
rlm@470 1155 relative sensitivity to the channels red, green, and blue. These
rlm@470 1156 sensitivity values are packed into an integer in the order
rlm@470 1157 =|_|R|G|B|= in 8-bit fields. The RGB values of a pixel in the
rlm@470 1158 image are added together with these sensitivities as linear
rlm@470 1159 weights. Therefore, 0xFF0000 means sensitive to red only while
rlm@470 1160 0xFFFFFF means sensitive to all colors equally (gray).
rlm@470 1161
rlm@470 1162 #+caption: This is the core of vision in =CORTEX=. A given eye node
rlm@470 1163 #+caption: is converted into a function that returns visual
rlm@470 1164 #+caption: information from the simulation.
rlm@471 1165 #+name: vision-kernel
rlm@470 1166 #+begin_listing clojure
rlm@470 1167 (defn vision-kernel
rlm@470 1168 "Returns a list of functions, each of which will return a color
rlm@470 1169 channel's worth of visual information when called inside a running
rlm@470 1170 simulation."
rlm@470 1171 [#^Node creature #^Spatial eye & {skip :skip :or {skip 0}}]
rlm@470 1172 (let [retinal-map (retina-sensor-profile eye)
rlm@470 1173 camera (add-eye! creature eye)
rlm@470 1174 vision-image
rlm@470 1175 (atom
rlm@470 1176 (BufferedImage. (.getWidth camera)
rlm@470 1177 (.getHeight camera)
rlm@470 1178 BufferedImage/TYPE_BYTE_BINARY))
rlm@470 1179 register-eye!
rlm@470 1180 (runonce
rlm@470 1181 (fn [world]
rlm@470 1182 (add-camera!
rlm@470 1183 world camera
rlm@470 1184 (let [counter (atom 0)]
rlm@470 1185 (fn [r fb bb bi]
rlm@470 1186 (if (zero? (rem (swap! counter inc) (inc skip)))
rlm@470 1187 (reset! vision-image
rlm@470 1188 (BufferedImage! r fb bb bi))))))))]
rlm@470 1189 (vec
rlm@470 1190 (map
rlm@470 1191 (fn [[key image]]
rlm@470 1192 (let [whites (white-coordinates image)
rlm@470 1193 topology (vec (collapse whites))
rlm@470 1194 sensitivity (sensitivity-presets key key)]
rlm@470 1195 (attached-viewport.
rlm@470 1196 (fn [world]
rlm@470 1197 (register-eye! world)
rlm@470 1198 (vector
rlm@470 1199 topology
rlm@470 1200 (vec
rlm@470 1201 (for [[x y] whites]
rlm@470 1202 (pixel-sense
rlm@470 1203 sensitivity
rlm@470 1204 (.getRGB @vision-image x y))))))
rlm@470 1205 register-eye!)))
rlm@470 1206 retinal-map))))
rlm@470 1207 #+end_listing
rlm@470 1208
rlm@470 1209 Note that since each of the functions generated by =vision-kernel=
rlm@470 1210 shares the same =register-eye!= function, the eye will be
rlm@470 1211 registered only once the first time any of the functions from the
rlm@470 1212 list returned by =vision-kernel= is called. Each of the functions
rlm@470 1213 returned by =vision-kernel= also allows access to the =Viewport=
rlm@470 1214 through which it receives images.
rlm@470 1215
rlm@470 1216 All the hard work has been done; all that remains is to apply
rlm@470 1217 =vision-kernel= to each eye in the creature and gather the results
rlm@470 1218 into one list of functions.
rlm@470 1219
rlm@470 1220
rlm@470 1221 #+caption: With =vision!=, =CORTEX= is already a fine simulation
rlm@470 1222 #+caption: environment for experimenting with different types of
rlm@470 1223 #+caption: eyes.
rlm@470 1224 #+name: vision!
rlm@470 1225 #+begin_listing clojure
rlm@470 1226 (defn vision!
rlm@470 1227 "Returns a list of functions, each of which returns visual sensory
rlm@470 1228 data when called inside a running simulation."
rlm@470 1229 [#^Node creature & {skip :skip :or {skip 0}}]
rlm@470 1230 (reduce
rlm@470 1231 concat
rlm@470 1232 (for [eye (eyes creature)]
rlm@470 1233 (vision-kernel creature eye))))
rlm@470 1234 #+end_listing
rlm@470 1235
rlm@471 1236 #+caption: Simulated vision with a test creature and the
rlm@471 1237 #+caption: human-like eye approximation. Notice how each channel
rlm@471 1238 #+caption: of the eye responds differently to the differently
rlm@471 1239 #+caption: colored balls.
rlm@471 1240 #+name: worm-vision-test.
rlm@471 1241 #+ATTR_LaTeX: :width 13cm
rlm@471 1242 [[./images/worm-vision.png]]
rlm@470 1243
rlm@471 1244 The vision code is not much more complicated than the body code,
rlm@471 1245 and enables multiple further paths for simulated vision. For
rlm@471 1246 example, it is quite easy to create bifocal vision -- you just
rlm@471 1247 make two eyes next to each other in blender! It is also possible
rlm@471 1248 to encode vision transforms in the retinal files. For example, the
rlm@471 1249 human like retina file in figure \ref{retina} approximates a
rlm@471 1250 log-polar transform.
rlm@470 1251
rlm@471 1252 This vision code has already been absorbed by the jMonkeyEngine
rlm@471 1253 community and is now (in modified form) part of a system for
rlm@471 1254 capturing in-game video to a file.
rlm@470 1255
rlm@473 1256 ** COMMENT Hearing is hard; =CORTEX= does it right
rlm@473 1257
rlm@472 1258 At the end of this section I will have simulated ears that work the
rlm@472 1259 same way as the simulated eyes in the last section. I will be able to
rlm@472 1260 place any number of ear-nodes in a blender file, and they will bind to
rlm@472 1261 the closest physical object and follow it as it moves around. Each ear
rlm@472 1262 will provide access to the sound data it picks up between every frame.
rlm@472 1263
rlm@472 1264 Hearing is one of the more difficult senses to simulate, because there
rlm@472 1265 is less support for obtaining the actual sound data that is processed
rlm@472 1266 by jMonkeyEngine3. There is no "split-screen" support for rendering
rlm@472 1267 sound from different points of view, and there is no way to directly
rlm@472 1268 access the rendered sound data.
rlm@472 1269
rlm@472 1270 =CORTEX='s hearing is unique because it does not have any
rlm@472 1271 limitations compared to other simulation environments. As far as I
rlm@472 1272 know, there is no other system that supports multiple listerers,
rlm@472 1273 and the sound demo at the end of this section is the first time
rlm@472 1274 it's been done in a video game environment.
rlm@472 1275
rlm@472 1276 *** Brief Description of jMonkeyEngine's Sound System
rlm@472 1277
rlm@472 1278 jMonkeyEngine's sound system works as follows:
rlm@472 1279
rlm@472 1280 - jMonkeyEngine uses the =AppSettings= for the particular
rlm@472 1281 application to determine what sort of =AudioRenderer= should be
rlm@472 1282 used.
rlm@472 1283 - Although some support is provided for multiple AudioRendering
rlm@472 1284 backends, jMonkeyEngine at the time of this writing will either
rlm@472 1285 pick no =AudioRenderer= at all, or the =LwjglAudioRenderer=.
rlm@472 1286 - jMonkeyEngine tries to figure out what sort of system you're
rlm@472 1287 running and extracts the appropriate native libraries.
rlm@472 1288 - The =LwjglAudioRenderer= uses the [[http://lwjgl.org/][=LWJGL=]] (LightWeight Java Game
rlm@472 1289 Library) bindings to interface with a C library called [[http://kcat.strangesoft.net/openal.html][=OpenAL=]]
rlm@472 1290 - =OpenAL= renders the 3D sound and feeds the rendered sound
rlm@472 1291 directly to any of various sound output devices with which it
rlm@472 1292 knows how to communicate.
rlm@472 1293
rlm@472 1294 A consequence of this is that there's no way to access the actual
rlm@472 1295 sound data produced by =OpenAL=. Even worse, =OpenAL= only supports
rlm@472 1296 one /listener/ (it renders sound data from only one perspective),
rlm@472 1297 which normally isn't a problem for games, but becomes a problem
rlm@472 1298 when trying to make multiple AI creatures that can each hear the
rlm@472 1299 world from a different perspective.
rlm@472 1300
rlm@472 1301 To make many AI creatures in jMonkeyEngine that can each hear the
rlm@472 1302 world from their own perspective, or to make a single creature with
rlm@472 1303 many ears, it is necessary to go all the way back to =OpenAL= and
rlm@472 1304 implement support for simulated hearing there.
rlm@472 1305
rlm@472 1306 *** Extending =OpenAl=
rlm@472 1307
rlm@472 1308 Extending =OpenAL= to support multiple listeners requires 500
rlm@472 1309 lines of =C= code and is too hairy to mention here. Instead, I
rlm@472 1310 will show a small amount of extension code and go over the high
rlm@472 1311 level stragety. Full source is of course available with the
rlm@472 1312 =CORTEX= distribution if you're interested.
rlm@472 1313
rlm@472 1314 =OpenAL= goes to great lengths to support many different systems,
rlm@472 1315 all with different sound capabilities and interfaces. It
rlm@472 1316 accomplishes this difficult task by providing code for many
rlm@472 1317 different sound backends in pseudo-objects called /Devices/.
rlm@472 1318 There's a device for the Linux Open Sound System and the Advanced
rlm@472 1319 Linux Sound Architecture, there's one for Direct Sound on Windows,
rlm@472 1320 and there's even one for Solaris. =OpenAL= solves the problem of
rlm@472 1321 platform independence by providing all these Devices.
rlm@472 1322
rlm@472 1323 Wrapper libraries such as LWJGL are free to examine the system on
rlm@472 1324 which they are running and then select an appropriate device for
rlm@472 1325 that system.
rlm@472 1326
rlm@472 1327 There are also a few "special" devices that don't interface with
rlm@472 1328 any particular system. These include the Null Device, which
rlm@472 1329 doesn't do anything, and the Wave Device, which writes whatever
rlm@472 1330 sound it receives to a file, if everything has been set up
rlm@472 1331 correctly when configuring =OpenAL=.
rlm@472 1332
rlm@472 1333 Actual mixing (doppler shift and distance.environment-based
rlm@472 1334 attenuation) of the sound data happens in the Devices, and they
rlm@472 1335 are the only point in the sound rendering process where this data
rlm@472 1336 is available.
rlm@472 1337
rlm@472 1338 Therefore, in order to support multiple listeners, and get the
rlm@472 1339 sound data in a form that the AIs can use, it is necessary to
rlm@472 1340 create a new Device which supports this feature.
rlm@472 1341
rlm@472 1342 Adding a device to OpenAL is rather tricky -- there are five
rlm@472 1343 separate files in the =OpenAL= source tree that must be modified
rlm@472 1344 to do so. I named my device the "Multiple Audio Send" Device, or
rlm@472 1345 =Send= Device for short, since it sends audio data back to the
rlm@472 1346 calling application like an Aux-Send cable on a mixing board.
rlm@472 1347
rlm@472 1348 The main idea behind the Send device is to take advantage of the
rlm@472 1349 fact that LWJGL only manages one /context/ when using OpenAL. A
rlm@472 1350 /context/ is like a container that holds samples and keeps track
rlm@472 1351 of where the listener is. In order to support multiple listeners,
rlm@472 1352 the Send device identifies the LWJGL context as the master
rlm@472 1353 context, and creates any number of slave contexts to represent
rlm@472 1354 additional listeners. Every time the device renders sound, it
rlm@472 1355 synchronizes every source from the master LWJGL context to the
rlm@472 1356 slave contexts. Then, it renders each context separately, using a
rlm@472 1357 different listener for each one. The rendered sound is made
rlm@472 1358 available via JNI to jMonkeyEngine.
rlm@472 1359
rlm@472 1360 Switching between contexts is not the normal operation of a
rlm@472 1361 Device, and one of the problems with doing so is that a Device
rlm@472 1362 normally keeps around a few pieces of state such as the
rlm@472 1363 =ClickRemoval= array above which will become corrupted if the
rlm@472 1364 contexts are not rendered in parallel. The solution is to create a
rlm@472 1365 copy of this normally global device state for each context, and
rlm@472 1366 copy it back and forth into and out of the actual device state
rlm@472 1367 whenever a context is rendered.
rlm@472 1368
rlm@472 1369 The core of the =Send= device is the =syncSources= function, which
rlm@472 1370 does the job of copying all relevant data from one context to
rlm@472 1371 another.
rlm@472 1372
rlm@472 1373 #+caption: Program for extending =OpenAL= to support multiple
rlm@472 1374 #+caption: listeners via context copying/switching.
rlm@472 1375 #+name: sync-openal-sources
rlm@472 1376 #+begin_listing C
rlm@472 1377 void syncSources(ALsource *masterSource, ALsource *slaveSource,
rlm@472 1378 ALCcontext *masterCtx, ALCcontext *slaveCtx){
rlm@472 1379 ALuint master = masterSource->source;
rlm@472 1380 ALuint slave = slaveSource->source;
rlm@472 1381 ALCcontext *current = alcGetCurrentContext();
rlm@472 1382
rlm@472 1383 syncSourcef(master,slave,masterCtx,slaveCtx,AL_PITCH);
rlm@472 1384 syncSourcef(master,slave,masterCtx,slaveCtx,AL_GAIN);
rlm@472 1385 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_DISTANCE);
rlm@472 1386 syncSourcef(master,slave,masterCtx,slaveCtx,AL_ROLLOFF_FACTOR);
rlm@472 1387 syncSourcef(master,slave,masterCtx,slaveCtx,AL_REFERENCE_DISTANCE);
rlm@472 1388 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MIN_GAIN);
rlm@472 1389 syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_GAIN);
rlm@472 1390 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_GAIN);
rlm@472 1391 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_INNER_ANGLE);
rlm@472 1392 syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_ANGLE);
rlm@472 1393 syncSourcef(master,slave,masterCtx,slaveCtx,AL_SEC_OFFSET);
rlm@472 1394 syncSourcef(master,slave,masterCtx,slaveCtx,AL_SAMPLE_OFFSET);
rlm@472 1395 syncSourcef(master,slave,masterCtx,slaveCtx,AL_BYTE_OFFSET);
rlm@472 1396
rlm@472 1397 syncSource3f(master,slave,masterCtx,slaveCtx,AL_POSITION);
rlm@472 1398 syncSource3f(master,slave,masterCtx,slaveCtx,AL_VELOCITY);
rlm@472 1399 syncSource3f(master,slave,masterCtx,slaveCtx,AL_DIRECTION);
rlm@472 1400
rlm@472 1401 syncSourcei(master,slave,masterCtx,slaveCtx,AL_SOURCE_RELATIVE);
rlm@472 1402 syncSourcei(master,slave,masterCtx,slaveCtx,AL_LOOPING);
rlm@472 1403
rlm@472 1404 alcMakeContextCurrent(masterCtx);
rlm@472 1405 ALint source_type;
rlm@472 1406 alGetSourcei(master, AL_SOURCE_TYPE, &source_type);
rlm@472 1407
rlm@472 1408 // Only static sources are currently synchronized!
rlm@472 1409 if (AL_STATIC == source_type){
rlm@472 1410 ALint master_buffer;
rlm@472 1411 ALint slave_buffer;
rlm@472 1412 alGetSourcei(master, AL_BUFFER, &master_buffer);
rlm@472 1413 alcMakeContextCurrent(slaveCtx);
rlm@472 1414 alGetSourcei(slave, AL_BUFFER, &slave_buffer);
rlm@472 1415 if (master_buffer != slave_buffer){
rlm@472 1416 alSourcei(slave, AL_BUFFER, master_buffer);
rlm@472 1417 }
rlm@472 1418 }
rlm@472 1419
rlm@472 1420 // Synchronize the state of the two sources.
rlm@472 1421 alcMakeContextCurrent(masterCtx);
rlm@472 1422 ALint masterState;
rlm@472 1423 ALint slaveState;
rlm@472 1424
rlm@472 1425 alGetSourcei(master, AL_SOURCE_STATE, &masterState);
rlm@472 1426 alcMakeContextCurrent(slaveCtx);
rlm@472 1427 alGetSourcei(slave, AL_SOURCE_STATE, &slaveState);
rlm@472 1428
rlm@472 1429 if (masterState != slaveState){
rlm@472 1430 switch (masterState){
rlm@472 1431 case AL_INITIAL : alSourceRewind(slave); break;
rlm@472 1432 case AL_PLAYING : alSourcePlay(slave); break;
rlm@472 1433 case AL_PAUSED : alSourcePause(slave); break;
rlm@472 1434 case AL_STOPPED : alSourceStop(slave); break;
rlm@472 1435 }
rlm@472 1436 }
rlm@472 1437 // Restore whatever context was previously active.
rlm@472 1438 alcMakeContextCurrent(current);
rlm@472 1439 }
rlm@472 1440 #+end_listing
rlm@472 1441
rlm@472 1442 With this special context-switching device, and some ugly JNI
rlm@472 1443 bindings that are not worth mentioning, =CORTEX= gains the ability
rlm@472 1444 to access multiple sound streams from =OpenAL=.
rlm@472 1445
rlm@472 1446 #+caption: Program to create an ear from a blender empty node. The ear
rlm@472 1447 #+caption: follows around the nearest physical object and passes
rlm@472 1448 #+caption: all sensory data to a continuation function.
rlm@472 1449 #+name: add-ear
rlm@472 1450 #+begin_listing clojure
rlm@472 1451 (defn add-ear!
rlm@472 1452 "Create a Listener centered on the current position of 'ear
rlm@472 1453 which follows the closest physical node in 'creature and
rlm@472 1454 sends sound data to 'continuation."
rlm@472 1455 [#^Application world #^Node creature #^Spatial ear continuation]
rlm@472 1456 (let [target (closest-node creature ear)
rlm@472 1457 lis (Listener.)
rlm@472 1458 audio-renderer (.getAudioRenderer world)
rlm@472 1459 sp (hearing-pipeline continuation)]
rlm@472 1460 (.setLocation lis (.getWorldTranslation ear))
rlm@472 1461 (.setRotation lis (.getWorldRotation ear))
rlm@472 1462 (bind-sense target lis)
rlm@472 1463 (update-listener-velocity! target lis)
rlm@472 1464 (.addListener audio-renderer lis)
rlm@472 1465 (.registerSoundProcessor audio-renderer lis sp)))
rlm@472 1466 #+end_listing
rlm@472 1467
rlm@472 1468
rlm@472 1469 The =Send= device, unlike most of the other devices in =OpenAL=,
rlm@472 1470 does not render sound unless asked. This enables the system to
rlm@472 1471 slow down or speed up depending on the needs of the AIs who are
rlm@472 1472 using it to listen. If the device tried to render samples in
rlm@472 1473 real-time, a complicated AI whose mind takes 100 seconds of
rlm@472 1474 computer time to simulate 1 second of AI-time would miss almost
rlm@472 1475 all of the sound in its environment!
rlm@472 1476
rlm@472 1477 #+caption: Program to enable arbitrary hearing in =CORTEX=
rlm@472 1478 #+name: hearing
rlm@472 1479 #+begin_listing clojure
rlm@472 1480 (defn hearing-kernel
rlm@472 1481 "Returns a function which returns auditory sensory data when called
rlm@472 1482 inside a running simulation."
rlm@472 1483 [#^Node creature #^Spatial ear]
rlm@472 1484 (let [hearing-data (atom [])
rlm@472 1485 register-listener!
rlm@472 1486 (runonce
rlm@472 1487 (fn [#^Application world]
rlm@472 1488 (add-ear!
rlm@472 1489 world creature ear
rlm@472 1490 (comp #(reset! hearing-data %)
rlm@472 1491 byteBuffer->pulse-vector))))]
rlm@472 1492 (fn [#^Application world]
rlm@472 1493 (register-listener! world)
rlm@472 1494 (let [data @hearing-data
rlm@472 1495 topology
rlm@472 1496 (vec (map #(vector % 0) (range 0 (count data))))]
rlm@472 1497 [topology data]))))
rlm@472 1498
rlm@472 1499 (defn hearing!
rlm@472 1500 "Endow the creature in a particular world with the sense of
rlm@472 1501 hearing. Will return a sequence of functions, one for each ear,
rlm@472 1502 which when called will return the auditory data from that ear."
rlm@472 1503 [#^Node creature]
rlm@472 1504 (for [ear (ears creature)]
rlm@472 1505 (hearing-kernel creature ear)))
rlm@472 1506 #+end_listing
rlm@472 1507
rlm@472 1508 Armed with these functions, =CORTEX= is able to test possibly the
rlm@472 1509 first ever instance of multiple listeners in a video game engine
rlm@472 1510 based simulation!
rlm@472 1511
rlm@472 1512 #+caption: Here a simple creature responds to sound by changing
rlm@472 1513 #+caption: its color from gray to green when the total volume
rlm@472 1514 #+caption: goes over a threshold.
rlm@472 1515 #+name: sound-test
rlm@472 1516 #+begin_listing java
rlm@472 1517 /**
rlm@472 1518 * Respond to sound! This is the brain of an AI entity that
rlm@472 1519 * hears its surroundings and reacts to them.
rlm@472 1520 */
rlm@472 1521 public void process(ByteBuffer audioSamples,
rlm@472 1522 int numSamples, AudioFormat format) {
rlm@472 1523 audioSamples.clear();
rlm@472 1524 byte[] data = new byte[numSamples];
rlm@472 1525 float[] out = new float[numSamples];
rlm@472 1526 audioSamples.get(data);
rlm@472 1527 FloatSampleTools.
rlm@472 1528 byte2floatInterleaved
rlm@472 1529 (data, 0, out, 0, numSamples/format.getFrameSize(), format);
rlm@472 1530
rlm@472 1531 float max = Float.NEGATIVE_INFINITY;
rlm@472 1532 for (float f : out){if (f > max) max = f;}
rlm@472 1533 audioSamples.clear();
rlm@472 1534
rlm@472 1535 if (max > 0.1){
rlm@472 1536 entity.getMaterial().setColor("Color", ColorRGBA.Green);
rlm@472 1537 }
rlm@472 1538 else {
rlm@472 1539 entity.getMaterial().setColor("Color", ColorRGBA.Gray);
rlm@472 1540 }
rlm@472 1541 #+end_listing
rlm@472 1542
rlm@472 1543 #+caption: First ever simulation of multiple listerners in =CORTEX=.
rlm@472 1544 #+caption: Each cube is a creature which processes sound data with
rlm@472 1545 #+caption: the =process= function from listing \ref{sound-test}.
rlm@472 1546 #+caption: the ball is constantally emiting a pure tone of
rlm@472 1547 #+caption: constant volume. As it approaches the cubes, they each
rlm@472 1548 #+caption: change color in response to the sound.
rlm@472 1549 #+name: sound-cubes.
rlm@472 1550 #+ATTR_LaTeX: :width 10cm
rlm@472 1551 [[./images/aurellem-gray.png]]
rlm@472 1552
rlm@472 1553 This system of hearing has also been co-opted by the
rlm@472 1554 jMonkeyEngine3 community and is used to record audio for demo
rlm@472 1555 videos.
rlm@472 1556
rlm@436 1557 ** Touch uses hundreds of hair-like elements
rlm@436 1558
rlm@474 1559 Touch is critical to navigation and spatial reasoning and as such I
rlm@474 1560 need a simulated version of it to give to my AI creatures.
rlm@474 1561
rlm@474 1562 Human skin has a wide array of touch sensors, each of which
rlm@474 1563 specialize in detecting different vibrational modes and pressures.
rlm@474 1564 These sensors can integrate a vast expanse of skin (i.e. your
rlm@474 1565 entire palm), or a tiny patch of skin at the tip of your finger.
rlm@474 1566 The hairs of the skin help detect objects before they even come
rlm@474 1567 into contact with the skin proper.
rlm@474 1568
rlm@474 1569 However, touch in my simulated world can not exactly correspond to
rlm@474 1570 human touch because my creatures are made out of completely rigid
rlm@474 1571 segments that don't deform like human skin.
rlm@474 1572
rlm@474 1573 Instead of measuring deformation or vibration, I surround each
rlm@474 1574 rigid part with a plenitude of hair-like objects (/feelers/) which
rlm@474 1575 do not interact with the physical world. Physical objects can pass
rlm@474 1576 through them with no effect. The feelers are able to tell when
rlm@474 1577 other objects pass through them, and they constantly report how
rlm@474 1578 much of their extent is covered. So even though the creature's body
rlm@474 1579 parts do not deform, the feelers create a margin around those body
rlm@474 1580 parts which achieves a sense of touch which is a hybrid between a
rlm@474 1581 human's sense of deformation and sense from hairs.
rlm@474 1582
rlm@474 1583 Implementing touch in jMonkeyEngine follows a different technical
rlm@474 1584 route than vision and hearing. Those two senses piggybacked off
rlm@474 1585 jMonkeyEngine's 3D audio and video rendering subsystems. To
rlm@474 1586 simulate touch, I use jMonkeyEngine's physics system to execute
rlm@474 1587 many small collision detections, one for each feeler. The placement
rlm@474 1588 of the feelers is determined by a UV-mapped image which shows where
rlm@474 1589 each feeler should be on the 3D surface of the body.
rlm@474 1590
rlm@475 1591 *** COMMENT Defining Touch Meta-Data in Blender
rlm@474 1592
rlm@474 1593 Each geometry can have a single UV map which describes the
rlm@474 1594 position of the feelers which will constitute its sense of touch.
rlm@474 1595 This image path is stored under the ``touch'' key. The image itself
rlm@474 1596 is black and white, with black meaning a feeler length of 0 (no
rlm@474 1597 feeler is present) and white meaning a feeler length of =scale=,
rlm@474 1598 which is a float stored under the key "scale".
rlm@474 1599
rlm@475 1600 #+caption: Touch does not use empty nodes, to store metadata,
rlm@475 1601 #+caption: because the metadata of each solid part of a
rlm@475 1602 #+caption: creature's body is sufficient.
rlm@475 1603 #+name: touch-meta-data
rlm@475 1604 #+begin_listing clojure
rlm@474 1605 (defn tactile-sensor-profile
rlm@474 1606 "Return the touch-sensor distribution image in BufferedImage format,
rlm@474 1607 or nil if it does not exist."
rlm@474 1608 [#^Geometry obj]
rlm@474 1609 (if-let [image-path (meta-data obj "touch")]
rlm@474 1610 (load-image image-path)))
rlm@474 1611
rlm@474 1612 (defn tactile-scale
rlm@474 1613 "Return the length of each feeler. Default scale is 0.01
rlm@474 1614 jMonkeyEngine units."
rlm@474 1615 [#^Geometry obj]
rlm@474 1616 (if-let [scale (meta-data obj "scale")]
rlm@474 1617 scale 0.1))
rlm@475 1618 #+end_listing
rlm@474 1619
rlm@475 1620 Here is an example of a UV-map which specifies the position of
rlm@475 1621 touch sensors along the surface of the upper segment of a fingertip.
rlm@474 1622
rlm@475 1623
rlm@475 1624 #+caption: This is the tactile-sensor-profile for the upper segment
rlm@475 1625 #+caption: of a fingertip. It defines regions of high touch sensitivity
rlm@475 1626 #+caption: (where there are many white pixels) and regions of low
rlm@475 1627 #+caption: sensitivity (where white pixels are sparse).
rlm@475 1628 #+name: fimgertip-UV
rlm@475 1629 #+ATTR_LaTeX: :width 10cm
rlm@474 1630 [[../images/finger-UV.png]]
rlm@474 1631
rlm@475 1632 *** COMMENT Implementation Summary
rlm@474 1633
rlm@474 1634 To simulate touch there are three conceptual steps. For each solid
rlm@474 1635 object in the creature, you first have to get UV image and scale
rlm@474 1636 parameter which define the position and length of the feelers.
rlm@474 1637 Then, you use the triangles which comprise the mesh and the UV
rlm@474 1638 data stored in the mesh to determine the world-space position and
rlm@474 1639 orientation of each feeler. Then once every frame, update these
rlm@474 1640 positions and orientations to match the current position and
rlm@474 1641 orientation of the object, and use physics collision detection to
rlm@474 1642 gather tactile data.
rlm@474 1643
rlm@474 1644 Extracting the meta-data has already been described. The third
rlm@474 1645 step, physics collision detection, is handled in =touch-kernel=.
rlm@474 1646 Translating the positions and orientations of the feelers from the
rlm@474 1647 UV-map to world-space is itself a three-step process.
rlm@474 1648
rlm@475 1649 - Find the triangles which make up the mesh in pixel-space and in
rlm@475 1650 world-space. (=triangles= =pixel-triangles=).
rlm@474 1651
rlm@475 1652 - Find the coordinates of each feeler in world-space. These are
rlm@475 1653 the origins of the feelers. (=feeler-origins=).
rlm@474 1654
rlm@475 1655 - Calculate the normals of the triangles in world space, and add
rlm@475 1656 them to each of the origins of the feelers. These are the
rlm@475 1657 normalized coordinates of the tips of the feelers.
rlm@475 1658 (=feeler-tips=).
rlm@474 1659
rlm@475 1660 *** COMMENT Triangle Math
rlm@474 1661
rlm@475 1662 The rigid objects which make up a creature have an underlying
rlm@475 1663 =Geometry=, which is a =Mesh= plus a =Material= and other
rlm@475 1664 important data involved with displaying the object.
rlm@475 1665
rlm@475 1666 A =Mesh= is composed of =Triangles=, and each =Triangle= has three
rlm@475 1667 vertices which have coordinates in world space and UV space.
rlm@475 1668
rlm@475 1669 Here, =triangles= gets all the world-space triangles which
rlm@475 1670 comprise a mesh, while =pixel-triangles= gets those same triangles
rlm@475 1671 expressed in pixel coordinates (which are UV coordinates scaled to
rlm@475 1672 fit the height and width of the UV image).
rlm@474 1673
rlm@475 1674 #+caption: Programs to extract triangles from a geometry and get
rlm@475 1675 #+caption: their verticies in both world and UV-coordinates.
rlm@475 1676 #+name: get-triangles
rlm@475 1677 #+begin_listing clojure
rlm@474 1678 (defn triangle
rlm@474 1679 "Get the triangle specified by triangle-index from the mesh."
rlm@474 1680 [#^Geometry geo triangle-index]
rlm@474 1681 (triangle-seq
rlm@474 1682 (let [scratch (Triangle.)]
rlm@474 1683 (.getTriangle (.getMesh geo) triangle-index scratch) scratch)))
rlm@474 1684
rlm@474 1685 (defn triangles
rlm@474 1686 "Return a sequence of all the Triangles which comprise a given
rlm@474 1687 Geometry."
rlm@474 1688 [#^Geometry geo]
rlm@474 1689 (map (partial triangle geo) (range (.getTriangleCount (.getMesh geo)))))
rlm@474 1690
rlm@474 1691 (defn triangle-vertex-indices
rlm@474 1692 "Get the triangle vertex indices of a given triangle from a given
rlm@474 1693 mesh."
rlm@474 1694 [#^Mesh mesh triangle-index]
rlm@474 1695 (let [indices (int-array 3)]
rlm@474 1696 (.getTriangle mesh triangle-index indices)
rlm@474 1697 (vec indices)))
rlm@474 1698
rlm@475 1699 (defn vertex-UV-coord
rlm@474 1700 "Get the UV-coordinates of the vertex named by vertex-index"
rlm@474 1701 [#^Mesh mesh vertex-index]
rlm@474 1702 (let [UV-buffer
rlm@474 1703 (.getData
rlm@474 1704 (.getBuffer
rlm@474 1705 mesh
rlm@474 1706 VertexBuffer$Type/TexCoord))]
rlm@474 1707 [(.get UV-buffer (* vertex-index 2))
rlm@474 1708 (.get UV-buffer (+ 1 (* vertex-index 2)))]))
rlm@474 1709
rlm@474 1710 (defn pixel-triangle [#^Geometry geo image index]
rlm@474 1711 (let [mesh (.getMesh geo)
rlm@474 1712 width (.getWidth image)
rlm@474 1713 height (.getHeight image)]
rlm@474 1714 (vec (map (fn [[u v]] (vector (* width u) (* height v)))
rlm@474 1715 (map (partial vertex-UV-coord mesh)
rlm@474 1716 (triangle-vertex-indices mesh index))))))
rlm@474 1717
rlm@474 1718 (defn pixel-triangles
rlm@474 1719 "The pixel-space triangles of the Geometry, in the same order as
rlm@474 1720 (triangles geo)"
rlm@474 1721 [#^Geometry geo image]
rlm@474 1722 (let [height (.getHeight image)
rlm@474 1723 width (.getWidth image)]
rlm@474 1724 (map (partial pixel-triangle geo image)
rlm@474 1725 (range (.getTriangleCount (.getMesh geo))))))
rlm@475 1726 #+end_listing
rlm@475 1727
rlm@474 1728 *** The Affine Transform from one Triangle to Another
rlm@474 1729
rlm@475 1730 =pixel-triangles= gives us the mesh triangles expressed in pixel
rlm@475 1731 coordinates and =triangles= gives us the mesh triangles expressed
rlm@475 1732 in world coordinates. The tactile-sensor-profile gives the
rlm@475 1733 position of each feeler in pixel-space. In order to convert
rlm@475 1734 pixel-space coordinates into world-space coordinates we need
rlm@475 1735 something that takes coordinates on the surface of one triangle
rlm@475 1736 and gives the corresponding coordinates on the surface of another
rlm@475 1737 triangle.
rlm@475 1738
rlm@475 1739 Triangles are [[http://mathworld.wolfram.com/AffineTransformation.html ][affine]], which means any triangle can be transformed
rlm@475 1740 into any other by a combination of translation, scaling, and
rlm@475 1741 rotation. The affine transformation from one triangle to another
rlm@475 1742 is readily computable if the triangle is expressed in terms of a
rlm@475 1743 $4x4$ matrix.
rlm@475 1744
rlm@475 1745 \begin{bmatrix}
rlm@475 1746 x_1 & x_2 & x_3 & n_x \\
rlm@475 1747 y_1 & y_2 & y_3 & n_y \\
rlm@475 1748 z_1 & z_2 & z_3 & n_z \\
rlm@475 1749 1 & 1 & 1 & 1
rlm@475 1750 \end{bmatrix}
rlm@475 1751
rlm@475 1752 Here, the first three columns of the matrix are the vertices of
rlm@475 1753 the triangle. The last column is the right-handed unit normal of
rlm@475 1754 the triangle.
rlm@475 1755
rlm@475 1756 With two triangles $T_{1}$ and $T_{2}$ each expressed as a matrix
rlm@475 1757 like above, the affine transform from $T_{1}$ to $T_{2}$ is
rlm@475 1758
rlm@475 1759 $T_{2}T_{1}^{-1}$
rlm@475 1760
rlm@475 1761 The clojure code below recapitulates the formulas above, using
rlm@475 1762 jMonkeyEngine's =Matrix4f= objects, which can describe any affine
rlm@475 1763 transformation.
rlm@474 1764
rlm@475 1765 #+caption: Program to interpert triangles as affine transforms.
rlm@475 1766 #+name: triangle-affine
rlm@475 1767 #+begin_listing clojure
rlm@475 1768 #+BEGIN_SRC clojure
rlm@474 1769 (defn triangle->matrix4f
rlm@474 1770 "Converts the triangle into a 4x4 matrix: The first three columns
rlm@474 1771 contain the vertices of the triangle; the last contains the unit
rlm@474 1772 normal of the triangle. The bottom row is filled with 1s."
rlm@474 1773 [#^Triangle t]
rlm@474 1774 (let [mat (Matrix4f.)
rlm@474 1775 [vert-1 vert-2 vert-3]
rlm@474 1776 (mapv #(.get t %) (range 3))
rlm@474 1777 unit-normal (do (.calculateNormal t)(.getNormal t))
rlm@474 1778 vertices [vert-1 vert-2 vert-3 unit-normal]]
rlm@474 1779 (dorun
rlm@474 1780 (for [row (range 4) col (range 3)]
rlm@474 1781 (do
rlm@474 1782 (.set mat col row (.get (vertices row) col))
rlm@474 1783 (.set mat 3 row 1)))) mat))
rlm@474 1784
rlm@474 1785 (defn triangles->affine-transform
rlm@474 1786 "Returns the affine transformation that converts each vertex in the
rlm@474 1787 first triangle into the corresponding vertex in the second
rlm@474 1788 triangle."
rlm@474 1789 [#^Triangle tri-1 #^Triangle tri-2]
rlm@474 1790 (.mult
rlm@474 1791 (triangle->matrix4f tri-2)
rlm@474 1792 (.invert (triangle->matrix4f tri-1))))
rlm@475 1793 #+END_SRC
rlm@475 1794 #+end_listing
rlm@474 1795
rlm@475 1796 *** COMMENT Triangle Boundaries
rlm@474 1797
rlm@474 1798 For efficiency's sake I will divide the tactile-profile image into
rlm@474 1799 small squares which inscribe each pixel-triangle, then extract the
rlm@474 1800 points which lie inside the triangle and map them to 3D-space using
rlm@474 1801 =triangle-transform= above. To do this I need a function,
rlm@474 1802 =convex-bounds= which finds the smallest box which inscribes a 2D
rlm@474 1803 triangle.
rlm@474 1804
rlm@474 1805 =inside-triangle?= determines whether a point is inside a triangle
rlm@474 1806 in 2D pixel-space.
rlm@474 1807
rlm@475 1808 #+caption: Program to efficiently determine point includion
rlm@475 1809 #+caption: in a triangle.
rlm@475 1810 #+name: in-triangle
rlm@475 1811 #+begin_listing clojure
rlm@475 1812 #+BEGIN_SRC clojure
rlm@474 1813 (defn convex-bounds
rlm@474 1814 "Returns the smallest square containing the given vertices, as a
rlm@474 1815 vector of integers [left top width height]."
rlm@474 1816 [verts]
rlm@474 1817 (let [xs (map first verts)
rlm@474 1818 ys (map second verts)
rlm@474 1819 x0 (Math/floor (apply min xs))
rlm@474 1820 y0 (Math/floor (apply min ys))
rlm@474 1821 x1 (Math/ceil (apply max xs))
rlm@474 1822 y1 (Math/ceil (apply max ys))]
rlm@474 1823 [x0 y0 (- x1 x0) (- y1 y0)]))
rlm@474 1824
rlm@474 1825 (defn same-side?
rlm@474 1826 "Given the points p1 and p2 and the reference point ref, is point p
rlm@474 1827 on the same side of the line that goes through p1 and p2 as ref is?"
rlm@474 1828 [p1 p2 ref p]
rlm@474 1829 (<=
rlm@474 1830 0
rlm@474 1831 (.dot
rlm@474 1832 (.cross (.subtract p2 p1) (.subtract p p1))
rlm@474 1833 (.cross (.subtract p2 p1) (.subtract ref p1)))))
rlm@474 1834
rlm@474 1835 (defn inside-triangle?
rlm@474 1836 "Is the point inside the triangle?"
rlm@474 1837 {:author "Dylan Holmes"}
rlm@474 1838 [#^Triangle tri #^Vector3f p]
rlm@474 1839 (let [[vert-1 vert-2 vert-3] [(.get1 tri) (.get2 tri) (.get3 tri)]]
rlm@474 1840 (and
rlm@474 1841 (same-side? vert-1 vert-2 vert-3 p)
rlm@474 1842 (same-side? vert-2 vert-3 vert-1 p)
rlm@474 1843 (same-side? vert-3 vert-1 vert-2 p))))
rlm@475 1844 #+END_SRC
rlm@475 1845 #+end_listing
rlm@474 1846
rlm@475 1847 *** COMMENT Feeler Coordinates
rlm@474 1848
rlm@475 1849 The triangle-related functions above make short work of
rlm@475 1850 calculating the positions and orientations of each feeler in
rlm@475 1851 world-space.
rlm@474 1852
rlm@475 1853 #+caption: Program to get the coordinates of ``feelers '' in
rlm@475 1854 #+caption: both world and UV-coordinates.
rlm@475 1855 #+name: feeler-coordinates
rlm@475 1856 #+begin_listing clojure
rlm@475 1857 #+BEGIN_SRC clojure
rlm@474 1858 (defn feeler-pixel-coords
rlm@474 1859 "Returns the coordinates of the feelers in pixel space in lists, one
rlm@474 1860 list for each triangle, ordered in the same way as (triangles) and
rlm@474 1861 (pixel-triangles)."
rlm@474 1862 [#^Geometry geo image]
rlm@474 1863 (map
rlm@474 1864 (fn [pixel-triangle]
rlm@474 1865 (filter
rlm@474 1866 (fn [coord]
rlm@474 1867 (inside-triangle? (->triangle pixel-triangle)
rlm@474 1868 (->vector3f coord)))
rlm@474 1869 (white-coordinates image (convex-bounds pixel-triangle))))
rlm@474 1870 (pixel-triangles geo image)))
rlm@474 1871
rlm@474 1872 (defn feeler-world-coords
rlm@474 1873 "Returns the coordinates of the feelers in world space in lists, one
rlm@474 1874 list for each triangle, ordered in the same way as (triangles) and
rlm@474 1875 (pixel-triangles)."
rlm@474 1876 [#^Geometry geo image]
rlm@474 1877 (let [transforms
rlm@474 1878 (map #(triangles->affine-transform
rlm@474 1879 (->triangle %1) (->triangle %2))
rlm@474 1880 (pixel-triangles geo image)
rlm@474 1881 (triangles geo))]
rlm@474 1882 (map (fn [transform coords]
rlm@474 1883 (map #(.mult transform (->vector3f %)) coords))
rlm@474 1884 transforms (feeler-pixel-coords geo image))))
rlm@475 1885 #+END_SRC
rlm@475 1886 #+end_listing
rlm@474 1887
rlm@475 1888 #+caption: Program to get the position of the base and tip of
rlm@475 1889 #+caption: each ``feeler''
rlm@475 1890 #+name: feeler-tips
rlm@475 1891 #+begin_listing clojure
rlm@475 1892 #+BEGIN_SRC clojure
rlm@474 1893 (defn feeler-origins
rlm@474 1894 "The world space coordinates of the root of each feeler."
rlm@474 1895 [#^Geometry geo image]
rlm@474 1896 (reduce concat (feeler-world-coords geo image)))
rlm@474 1897
rlm@474 1898 (defn feeler-tips
rlm@474 1899 "The world space coordinates of the tip of each feeler."
rlm@474 1900 [#^Geometry geo image]
rlm@474 1901 (let [world-coords (feeler-world-coords geo image)
rlm@474 1902 normals
rlm@474 1903 (map
rlm@474 1904 (fn [triangle]
rlm@474 1905 (.calculateNormal triangle)
rlm@474 1906 (.clone (.getNormal triangle)))
rlm@474 1907 (map ->triangle (triangles geo)))]
rlm@474 1908
rlm@474 1909 (mapcat (fn [origins normal]
rlm@474 1910 (map #(.add % normal) origins))
rlm@474 1911 world-coords normals)))
rlm@474 1912
rlm@474 1913 (defn touch-topology
rlm@474 1914 [#^Geometry geo image]
rlm@474 1915 (collapse (reduce concat (feeler-pixel-coords geo image))))
rlm@475 1916 #+END_SRC
rlm@475 1917 #+end_listing
rlm@474 1918
rlm@475 1919 *** COMMENT Simulated Touch
rlm@474 1920
rlm@475 1921 Now that the functions to construct feelers are complete,
rlm@475 1922 =touch-kernel= generates functions to be called from within a
rlm@475 1923 simulation that perform the necessary physics collisions to
rlm@475 1924 collect tactile data, and =touch!= recursively applies it to every
rlm@475 1925 node in the creature.
rlm@474 1926
rlm@475 1927 #+caption: Efficient program to transform a ray from
rlm@475 1928 #+caption: one position to another.
rlm@475 1929 #+name: set-ray
rlm@475 1930 #+begin_listing clojure
rlm@475 1931 #+BEGIN_SRC clojure
rlm@474 1932 (defn set-ray [#^Ray ray #^Matrix4f transform
rlm@474 1933 #^Vector3f origin #^Vector3f tip]
rlm@474 1934 ;; Doing everything locally reduces garbage collection by enough to
rlm@474 1935 ;; be worth it.
rlm@474 1936 (.mult transform origin (.getOrigin ray))
rlm@474 1937 (.mult transform tip (.getDirection ray))
rlm@474 1938 (.subtractLocal (.getDirection ray) (.getOrigin ray))
rlm@474 1939 (.normalizeLocal (.getDirection ray)))
rlm@475 1940 #+END_SRC
rlm@475 1941 #+end_listing
rlm@474 1942
rlm@475 1943 #+caption: This is the core of touch in =CORTEX= each feeler
rlm@475 1944 #+caption: follows the object it is bound to, reporting any
rlm@475 1945 #+caption: collisions that may happen.
rlm@475 1946 #+name: touch-kernel
rlm@475 1947 #+begin_listing clojure
rlm@475 1948 #+BEGIN_SRC clojure
rlm@474 1949 (defn touch-kernel
rlm@474 1950 "Constructs a function which will return tactile sensory data from
rlm@474 1951 'geo when called from inside a running simulation"
rlm@474 1952 [#^Geometry geo]
rlm@474 1953 (if-let
rlm@474 1954 [profile (tactile-sensor-profile geo)]
rlm@474 1955 (let [ray-reference-origins (feeler-origins geo profile)
rlm@474 1956 ray-reference-tips (feeler-tips geo profile)
rlm@474 1957 ray-length (tactile-scale geo)
rlm@474 1958 current-rays (map (fn [_] (Ray.)) ray-reference-origins)
rlm@474 1959 topology (touch-topology geo profile)
rlm@474 1960 correction (float (* ray-length -0.2))]
rlm@474 1961 ;; slight tolerance for very close collisions.
rlm@474 1962 (dorun
rlm@474 1963 (map (fn [origin tip]
rlm@474 1964 (.addLocal origin (.mult (.subtract tip origin)
rlm@474 1965 correction)))
rlm@474 1966 ray-reference-origins ray-reference-tips))
rlm@474 1967 (dorun (map #(.setLimit % ray-length) current-rays))
rlm@474 1968 (fn [node]
rlm@474 1969 (let [transform (.getWorldMatrix geo)]
rlm@474 1970 (dorun
rlm@474 1971 (map (fn [ray ref-origin ref-tip]
rlm@474 1972 (set-ray ray transform ref-origin ref-tip))
rlm@474 1973 current-rays ray-reference-origins
rlm@474 1974 ray-reference-tips))
rlm@474 1975 (vector
rlm@474 1976 topology
rlm@474 1977 (vec
rlm@474 1978 (for [ray current-rays]
rlm@474 1979 (do
rlm@474 1980 (let [results (CollisionResults.)]
rlm@474 1981 (.collideWith node ray results)
rlm@474 1982 (let [touch-objects
rlm@474 1983 (filter #(not (= geo (.getGeometry %)))
rlm@474 1984 results)
rlm@474 1985 limit (.getLimit ray)]
rlm@474 1986 [(if (empty? touch-objects)
rlm@474 1987 limit
rlm@474 1988 (let [response
rlm@474 1989 (apply min (map #(.getDistance %)
rlm@474 1990 touch-objects))]
rlm@474 1991 (FastMath/clamp
rlm@474 1992 (float
rlm@474 1993 (if (> response limit) (float 0.0)
rlm@474 1994 (+ response correction)))
rlm@474 1995 (float 0.0)
rlm@474 1996 limit)))
rlm@474 1997 limit])))))))))))
rlm@475 1998 #+END_SRC
rlm@475 1999 #+end_listing
rlm@474 2000
rlm@475 2001 Armed with the =touch!= function, =CORTEX= becomes capable of
rlm@475 2002 giving creatures a sense of touch. A simple test is to create a
rlm@475 2003 cube that is outfitted with a uniform distrubition of touch
rlm@475 2004 sensors. It can feel the ground and any balls that it touches.
rlm@475 2005
rlm@475 2006 #+caption: =CORTEX= interface for creating touch in a simulated
rlm@475 2007 #+caption: creature.
rlm@475 2008 #+name: touch
rlm@475 2009 #+begin_listing clojure
rlm@475 2010 #+BEGIN_SRC clojure
rlm@474 2011 (defn touch!
rlm@474 2012 "Endow the creature with the sense of touch. Returns a sequence of
rlm@474 2013 functions, one for each body part with a tactile-sensor-profile,
rlm@474 2014 each of which when called returns sensory data for that body part."
rlm@474 2015 [#^Node creature]
rlm@474 2016 (filter
rlm@474 2017 (comp not nil?)
rlm@474 2018 (map touch-kernel
rlm@474 2019 (filter #(isa? (class %) Geometry)
rlm@474 2020 (node-seq creature)))))
rlm@475 2021 #+END_SRC
rlm@475 2022 #+end_listing
rlm@475 2023
rlm@475 2024 The tactile-sensor-profile image for the touch cube is a simple
rlm@475 2025 cross with a unifom distribution of touch sensors:
rlm@474 2026
rlm@475 2027 #+caption: The touch profile for the touch-cube. Each pure white
rlm@475 2028 #+caption: pixel defines a touch sensitive feeler.
rlm@475 2029 #+name: touch-cube-uv-map
rlm@475 2030 #+ATTR_LaTeX: :width 10cm
rlm@475 2031 [[./images/touch-profile.png]]
rlm@474 2032
rlm@475 2033 #+caption: The touch cube reacts to canonballs. The black, red,
rlm@475 2034 #+caption: and white cross on the right is a visual display of
rlm@475 2035 #+caption: the creature's touch. White means that it is feeling
rlm@475 2036 #+caption: something strongly, black is not feeling anything,
rlm@475 2037 #+caption: and gray is in-between. The cube can feel both the
rlm@475 2038 #+caption: floor and the ball. Notice that when the ball causes
rlm@475 2039 #+caption: the cube to tip, that the bottom face can still feel
rlm@475 2040 #+caption: part of the ground.
rlm@475 2041 #+name: touch-cube-uv-map
rlm@475 2042 #+ATTR_LaTeX: :width 15cm
rlm@475 2043 [[./images/touch-cube.png]]
rlm@474 2044
rlm@440 2045 ** Proprioception is the sense that makes everything ``real''
rlm@436 2046
rlm@436 2047 ** Muscles are both effectors and sensors
rlm@436 2048
rlm@436 2049 ** =CORTEX= brings complex creatures to life!
rlm@436 2050
rlm@436 2051 ** =CORTEX= enables many possiblities for further research
rlm@474 2052
rlm@465 2053 * COMMENT Empathy in a simulated worm
rlm@435 2054
rlm@449 2055 Here I develop a computational model of empathy, using =CORTEX= as a
rlm@449 2056 base. Empathy in this context is the ability to observe another
rlm@449 2057 creature and infer what sorts of sensations that creature is
rlm@449 2058 feeling. My empathy algorithm involves multiple phases. First is
rlm@449 2059 free-play, where the creature moves around and gains sensory
rlm@449 2060 experience. From this experience I construct a representation of the
rlm@449 2061 creature's sensory state space, which I call \Phi-space. Using
rlm@449 2062 \Phi-space, I construct an efficient function which takes the
rlm@449 2063 limited data that comes from observing another creature and enriches
rlm@449 2064 it full compliment of imagined sensory data. I can then use the
rlm@449 2065 imagined sensory data to recognize what the observed creature is
rlm@449 2066 doing and feeling, using straightforward embodied action predicates.
rlm@449 2067 This is all demonstrated with using a simple worm-like creature, and
rlm@449 2068 recognizing worm-actions based on limited data.
rlm@449 2069
rlm@449 2070 #+caption: Here is the worm with which we will be working.
rlm@449 2071 #+caption: It is composed of 5 segments. Each segment has a
rlm@449 2072 #+caption: pair of extensor and flexor muscles. Each of the
rlm@449 2073 #+caption: worm's four joints is a hinge joint which allows
rlm@451 2074 #+caption: about 30 degrees of rotation to either side. Each segment
rlm@449 2075 #+caption: of the worm is touch-capable and has a uniform
rlm@449 2076 #+caption: distribution of touch sensors on each of its faces.
rlm@449 2077 #+caption: Each joint has a proprioceptive sense to detect
rlm@449 2078 #+caption: relative positions. The worm segments are all the
rlm@449 2079 #+caption: same except for the first one, which has a much
rlm@449 2080 #+caption: higher weight than the others to allow for easy
rlm@449 2081 #+caption: manual motor control.
rlm@449 2082 #+name: basic-worm-view
rlm@449 2083 #+ATTR_LaTeX: :width 10cm
rlm@449 2084 [[./images/basic-worm-view.png]]
rlm@449 2085
rlm@449 2086 #+caption: Program for reading a worm from a blender file and
rlm@449 2087 #+caption: outfitting it with the senses of proprioception,
rlm@449 2088 #+caption: touch, and the ability to move, as specified in the
rlm@449 2089 #+caption: blender file.
rlm@449 2090 #+name: get-worm
rlm@449 2091 #+begin_listing clojure
rlm@449 2092 #+begin_src clojure
rlm@449 2093 (defn worm []
rlm@449 2094 (let [model (load-blender-model "Models/worm/worm.blend")]
rlm@449 2095 {:body (doto model (body!))
rlm@449 2096 :touch (touch! model)
rlm@449 2097 :proprioception (proprioception! model)
rlm@449 2098 :muscles (movement! model)}))
rlm@449 2099 #+end_src
rlm@449 2100 #+end_listing
rlm@452 2101
rlm@436 2102 ** Embodiment factors action recognition into managable parts
rlm@435 2103
rlm@449 2104 Using empathy, I divide the problem of action recognition into a
rlm@449 2105 recognition process expressed in the language of a full compliment
rlm@449 2106 of senses, and an imaganitive process that generates full sensory
rlm@449 2107 data from partial sensory data. Splitting the action recognition
rlm@449 2108 problem in this manner greatly reduces the total amount of work to
rlm@449 2109 recognize actions: The imaganitive process is mostly just matching
rlm@449 2110 previous experience, and the recognition process gets to use all
rlm@449 2111 the senses to directly describe any action.
rlm@449 2112
rlm@436 2113 ** Action recognition is easy with a full gamut of senses
rlm@435 2114
rlm@449 2115 Embodied representations using multiple senses such as touch,
rlm@449 2116 proprioception, and muscle tension turns out be be exceedingly
rlm@449 2117 efficient at describing body-centered actions. It is the ``right
rlm@449 2118 language for the job''. For example, it takes only around 5 lines
rlm@449 2119 of LISP code to describe the action of ``curling'' using embodied
rlm@451 2120 primitives. It takes about 10 lines to describe the seemingly
rlm@449 2121 complicated action of wiggling.
rlm@449 2122
rlm@449 2123 The following action predicates each take a stream of sensory
rlm@449 2124 experience, observe however much of it they desire, and decide
rlm@449 2125 whether the worm is doing the action they describe. =curled?=
rlm@449 2126 relies on proprioception, =resting?= relies on touch, =wiggling?=
rlm@449 2127 relies on a fourier analysis of muscle contraction, and
rlm@449 2128 =grand-circle?= relies on touch and reuses =curled?= as a gaurd.
rlm@449 2129
rlm@449 2130 #+caption: Program for detecting whether the worm is curled. This is the
rlm@449 2131 #+caption: simplest action predicate, because it only uses the last frame
rlm@449 2132 #+caption: of sensory experience, and only uses proprioceptive data. Even
rlm@449 2133 #+caption: this simple predicate, however, is automatically frame
rlm@449 2134 #+caption: independent and ignores vermopomorphic differences such as
rlm@449 2135 #+caption: worm textures and colors.
rlm@449 2136 #+name: curled
rlm@452 2137 #+attr_latex: [htpb]
rlm@452 2138 #+begin_listing clojure
rlm@449 2139 #+begin_src clojure
rlm@449 2140 (defn curled?
rlm@449 2141 "Is the worm curled up?"
rlm@449 2142 [experiences]
rlm@449 2143 (every?
rlm@449 2144 (fn [[_ _ bend]]
rlm@449 2145 (> (Math/sin bend) 0.64))
rlm@449 2146 (:proprioception (peek experiences))))
rlm@449 2147 #+end_src
rlm@449 2148 #+end_listing
rlm@449 2149
rlm@449 2150 #+caption: Program for summarizing the touch information in a patch
rlm@449 2151 #+caption: of skin.
rlm@449 2152 #+name: touch-summary
rlm@452 2153 #+attr_latex: [htpb]
rlm@452 2154
rlm@452 2155 #+begin_listing clojure
rlm@449 2156 #+begin_src clojure
rlm@449 2157 (defn contact
rlm@449 2158 "Determine how much contact a particular worm segment has with
rlm@449 2159 other objects. Returns a value between 0 and 1, where 1 is full
rlm@449 2160 contact and 0 is no contact."
rlm@449 2161 [touch-region [coords contact :as touch]]
rlm@449 2162 (-> (zipmap coords contact)
rlm@449 2163 (select-keys touch-region)
rlm@449 2164 (vals)
rlm@449 2165 (#(map first %))
rlm@449 2166 (average)
rlm@449 2167 (* 10)
rlm@449 2168 (- 1)
rlm@449 2169 (Math/abs)))
rlm@449 2170 #+end_src
rlm@449 2171 #+end_listing
rlm@449 2172
rlm@449 2173
rlm@449 2174 #+caption: Program for detecting whether the worm is at rest. This program
rlm@449 2175 #+caption: uses a summary of the tactile information from the underbelly
rlm@449 2176 #+caption: of the worm, and is only true if every segment is touching the
rlm@449 2177 #+caption: floor. Note that this function contains no references to
rlm@449 2178 #+caption: proprioction at all.
rlm@449 2179 #+name: resting
rlm@452 2180 #+attr_latex: [htpb]
rlm@452 2181 #+begin_listing clojure
rlm@449 2182 #+begin_src clojure
rlm@449 2183 (def worm-segment-bottom (rect-region [8 15] [14 22]))
rlm@449 2184
rlm@449 2185 (defn resting?
rlm@449 2186 "Is the worm resting on the ground?"
rlm@449 2187 [experiences]
rlm@449 2188 (every?
rlm@449 2189 (fn [touch-data]
rlm@449 2190 (< 0.9 (contact worm-segment-bottom touch-data)))
rlm@449 2191 (:touch (peek experiences))))
rlm@449 2192 #+end_src
rlm@449 2193 #+end_listing
rlm@449 2194
rlm@449 2195 #+caption: Program for detecting whether the worm is curled up into a
rlm@449 2196 #+caption: full circle. Here the embodied approach begins to shine, as
rlm@449 2197 #+caption: I am able to both use a previous action predicate (=curled?=)
rlm@449 2198 #+caption: as well as the direct tactile experience of the head and tail.
rlm@449 2199 #+name: grand-circle
rlm@452 2200 #+attr_latex: [htpb]
rlm@452 2201 #+begin_listing clojure
rlm@449 2202 #+begin_src clojure
rlm@449 2203 (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))
rlm@449 2204
rlm@449 2205 (def worm-segment-top-tip (rect-region [0 15] [7 22]))
rlm@449 2206
rlm@449 2207 (defn grand-circle?
rlm@449 2208 "Does the worm form a majestic circle (one end touching the other)?"
rlm@449 2209 [experiences]
rlm@449 2210 (and (curled? experiences)
rlm@449 2211 (let [worm-touch (:touch (peek experiences))
rlm@449 2212 tail-touch (worm-touch 0)
rlm@449 2213 head-touch (worm-touch 4)]
rlm@449 2214 (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
rlm@449 2215 (< 0.55 (contact worm-segment-top-tip head-touch))))))
rlm@449 2216 #+end_src
rlm@449 2217 #+end_listing
rlm@449 2218
rlm@449 2219
rlm@449 2220 #+caption: Program for detecting whether the worm has been wiggling for
rlm@449 2221 #+caption: the last few frames. It uses a fourier analysis of the muscle
rlm@449 2222 #+caption: contractions of the worm's tail to determine wiggling. This is
rlm@449 2223 #+caption: signigicant because there is no particular frame that clearly
rlm@449 2224 #+caption: indicates that the worm is wiggling --- only when multiple frames
rlm@449 2225 #+caption: are analyzed together is the wiggling revealed. Defining
rlm@449 2226 #+caption: wiggling this way also gives the worm an opportunity to learn
rlm@449 2227 #+caption: and recognize ``frustrated wiggling'', where the worm tries to
rlm@449 2228 #+caption: wiggle but can't. Frustrated wiggling is very visually different
rlm@449 2229 #+caption: from actual wiggling, but this definition gives it to us for free.
rlm@449 2230 #+name: wiggling
rlm@452 2231 #+attr_latex: [htpb]
rlm@452 2232 #+begin_listing clojure
rlm@449 2233 #+begin_src clojure
rlm@449 2234 (defn fft [nums]
rlm@449 2235 (map
rlm@449 2236 #(.getReal %)
rlm@449 2237 (.transform
rlm@449 2238 (FastFourierTransformer. DftNormalization/STANDARD)
rlm@449 2239 (double-array nums) TransformType/FORWARD)))
rlm@449 2240
rlm@449 2241 (def indexed (partial map-indexed vector))
rlm@449 2242
rlm@449 2243 (defn max-indexed [s]
rlm@449 2244 (first (sort-by (comp - second) (indexed s))))
rlm@449 2245
rlm@449 2246 (defn wiggling?
rlm@449 2247 "Is the worm wiggling?"
rlm@449 2248 [experiences]
rlm@449 2249 (let [analysis-interval 0x40]
rlm@449 2250 (when (> (count experiences) analysis-interval)
rlm@449 2251 (let [a-flex 3
rlm@449 2252 a-ex 2
rlm@449 2253 muscle-activity
rlm@449 2254 (map :muscle (vector:last-n experiences analysis-interval))
rlm@449 2255 base-activity
rlm@449 2256 (map #(- (% a-flex) (% a-ex)) muscle-activity)]
rlm@449 2257 (= 2
rlm@449 2258 (first
rlm@449 2259 (max-indexed
rlm@449 2260 (map #(Math/abs %)
rlm@449 2261 (take 20 (fft base-activity))))))))))
rlm@449 2262 #+end_src
rlm@449 2263 #+end_listing
rlm@449 2264
rlm@449 2265 With these action predicates, I can now recognize the actions of
rlm@449 2266 the worm while it is moving under my control and I have access to
rlm@449 2267 all the worm's senses.
rlm@449 2268
rlm@449 2269 #+caption: Use the action predicates defined earlier to report on
rlm@449 2270 #+caption: what the worm is doing while in simulation.
rlm@449 2271 #+name: report-worm-activity
rlm@452 2272 #+attr_latex: [htpb]
rlm@452 2273 #+begin_listing clojure
rlm@449 2274 #+begin_src clojure
rlm@449 2275 (defn debug-experience
rlm@449 2276 [experiences text]
rlm@449 2277 (cond
rlm@449 2278 (grand-circle? experiences) (.setText text "Grand Circle")
rlm@449 2279 (curled? experiences) (.setText text "Curled")
rlm@449 2280 (wiggling? experiences) (.setText text "Wiggling")
rlm@449 2281 (resting? experiences) (.setText text "Resting")))
rlm@449 2282 #+end_src
rlm@449 2283 #+end_listing
rlm@449 2284
rlm@449 2285 #+caption: Using =debug-experience=, the body-centered predicates
rlm@449 2286 #+caption: work together to classify the behaviour of the worm.
rlm@451 2287 #+caption: the predicates are operating with access to the worm's
rlm@451 2288 #+caption: full sensory data.
rlm@449 2289 #+name: basic-worm-view
rlm@449 2290 #+ATTR_LaTeX: :width 10cm
rlm@449 2291 [[./images/worm-identify-init.png]]
rlm@449 2292
rlm@449 2293 These action predicates satisfy the recognition requirement of an
rlm@451 2294 empathic recognition system. There is power in the simplicity of
rlm@451 2295 the action predicates. They describe their actions without getting
rlm@451 2296 confused in visual details of the worm. Each one is frame
rlm@451 2297 independent, but more than that, they are each indepent of
rlm@449 2298 irrelevant visual details of the worm and the environment. They
rlm@449 2299 will work regardless of whether the worm is a different color or
rlm@451 2300 hevaily textured, or if the environment has strange lighting.
rlm@449 2301
rlm@449 2302 The trick now is to make the action predicates work even when the
rlm@449 2303 sensory data on which they depend is absent. If I can do that, then
rlm@449 2304 I will have gained much,
rlm@435 2305
rlm@436 2306 ** \Phi-space describes the worm's experiences
rlm@449 2307
rlm@449 2308 As a first step towards building empathy, I need to gather all of
rlm@449 2309 the worm's experiences during free play. I use a simple vector to
rlm@449 2310 store all the experiences.
rlm@449 2311
rlm@449 2312 Each element of the experience vector exists in the vast space of
rlm@449 2313 all possible worm-experiences. Most of this vast space is actually
rlm@449 2314 unreachable due to physical constraints of the worm's body. For
rlm@449 2315 example, the worm's segments are connected by hinge joints that put
rlm@451 2316 a practical limit on the worm's range of motions without limiting
rlm@451 2317 its degrees of freedom. Some groupings of senses are impossible;
rlm@451 2318 the worm can not be bent into a circle so that its ends are
rlm@451 2319 touching and at the same time not also experience the sensation of
rlm@451 2320 touching itself.
rlm@449 2321
rlm@451 2322 As the worm moves around during free play and its experience vector
rlm@451 2323 grows larger, the vector begins to define a subspace which is all
rlm@451 2324 the sensations the worm can practicaly experience during normal
rlm@451 2325 operation. I call this subspace \Phi-space, short for
rlm@451 2326 physical-space. The experience vector defines a path through
rlm@451 2327 \Phi-space. This path has interesting properties that all derive
rlm@451 2328 from physical embodiment. The proprioceptive components are
rlm@451 2329 completely smooth, because in order for the worm to move from one
rlm@451 2330 position to another, it must pass through the intermediate
rlm@451 2331 positions. The path invariably forms loops as actions are repeated.
rlm@451 2332 Finally and most importantly, proprioception actually gives very
rlm@451 2333 strong inference about the other senses. For example, when the worm
rlm@451 2334 is flat, you can infer that it is touching the ground and that its
rlm@451 2335 muscles are not active, because if the muscles were active, the
rlm@451 2336 worm would be moving and would not be perfectly flat. In order to
rlm@451 2337 stay flat, the worm has to be touching the ground, or it would
rlm@451 2338 again be moving out of the flat position due to gravity. If the
rlm@451 2339 worm is positioned in such a way that it interacts with itself,
rlm@451 2340 then it is very likely to be feeling the same tactile feelings as
rlm@451 2341 the last time it was in that position, because it has the same body
rlm@451 2342 as then. If you observe multiple frames of proprioceptive data,
rlm@451 2343 then you can become increasingly confident about the exact
rlm@451 2344 activations of the worm's muscles, because it generally takes a
rlm@451 2345 unique combination of muscle contractions to transform the worm's
rlm@451 2346 body along a specific path through \Phi-space.
rlm@449 2347
rlm@449 2348 There is a simple way of taking \Phi-space and the total ordering
rlm@449 2349 provided by an experience vector and reliably infering the rest of
rlm@449 2350 the senses.
rlm@435 2351
rlm@436 2352 ** Empathy is the process of tracing though \Phi-space
rlm@449 2353
rlm@450 2354 Here is the core of a basic empathy algorithm, starting with an
rlm@451 2355 experience vector:
rlm@451 2356
rlm@451 2357 First, group the experiences into tiered proprioceptive bins. I use
rlm@451 2358 powers of 10 and 3 bins, and the smallest bin has an approximate
rlm@451 2359 size of 0.001 radians in all proprioceptive dimensions.
rlm@450 2360
rlm@450 2361 Then, given a sequence of proprioceptive input, generate a set of
rlm@451 2362 matching experience records for each input, using the tiered
rlm@451 2363 proprioceptive bins.
rlm@449 2364
rlm@450 2365 Finally, to infer sensory data, select the longest consective chain
rlm@451 2366 of experiences. Conecutive experience means that the experiences
rlm@451 2367 appear next to each other in the experience vector.
rlm@449 2368
rlm@450 2369 This algorithm has three advantages:
rlm@450 2370
rlm@450 2371 1. It's simple
rlm@450 2372
rlm@451 2373 3. It's very fast -- retrieving possible interpretations takes
rlm@451 2374 constant time. Tracing through chains of interpretations takes
rlm@451 2375 time proportional to the average number of experiences in a
rlm@451 2376 proprioceptive bin. Redundant experiences in \Phi-space can be
rlm@451 2377 merged to save computation.
rlm@450 2378
rlm@450 2379 2. It protects from wrong interpretations of transient ambiguous
rlm@451 2380 proprioceptive data. For example, if the worm is flat for just
rlm@450 2381 an instant, this flattness will not be interpreted as implying
rlm@450 2382 that the worm has its muscles relaxed, since the flattness is
rlm@450 2383 part of a longer chain which includes a distinct pattern of
rlm@451 2384 muscle activation. Markov chains or other memoryless statistical
rlm@451 2385 models that operate on individual frames may very well make this
rlm@451 2386 mistake.
rlm@450 2387
rlm@450 2388 #+caption: Program to convert an experience vector into a
rlm@450 2389 #+caption: proprioceptively binned lookup function.
rlm@450 2390 #+name: bin
rlm@452 2391 #+attr_latex: [htpb]
rlm@452 2392 #+begin_listing clojure
rlm@450 2393 #+begin_src clojure
rlm@449 2394 (defn bin [digits]
rlm@449 2395 (fn [angles]
rlm@449 2396 (->> angles
rlm@449 2397 (flatten)
rlm@449 2398 (map (juxt #(Math/sin %) #(Math/cos %)))
rlm@449 2399 (flatten)
rlm@449 2400 (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))
rlm@449 2401
rlm@449 2402 (defn gen-phi-scan
rlm@450 2403 "Nearest-neighbors with binning. Only returns a result if
rlm@450 2404 the propriceptive data is within 10% of a previously recorded
rlm@450 2405 result in all dimensions."
rlm@450 2406 [phi-space]
rlm@449 2407 (let [bin-keys (map bin [3 2 1])
rlm@449 2408 bin-maps
rlm@449 2409 (map (fn [bin-key]
rlm@449 2410 (group-by
rlm@449 2411 (comp bin-key :proprioception phi-space)
rlm@449 2412 (range (count phi-space)))) bin-keys)
rlm@449 2413 lookups (map (fn [bin-key bin-map]
rlm@450 2414 (fn [proprio] (bin-map (bin-key proprio))))
rlm@450 2415 bin-keys bin-maps)]
rlm@449 2416 (fn lookup [proprio-data]
rlm@449 2417 (set (some #(% proprio-data) lookups)))))
rlm@450 2418 #+end_src
rlm@450 2419 #+end_listing
rlm@449 2420
rlm@451 2421 #+caption: =longest-thread= finds the longest path of consecutive
rlm@451 2422 #+caption: experiences to explain proprioceptive worm data.
rlm@451 2423 #+name: phi-space-history-scan
rlm@451 2424 #+ATTR_LaTeX: :width 10cm
rlm@451 2425 [[./images/aurellem-gray.png]]
rlm@451 2426
rlm@451 2427 =longest-thread= infers sensory data by stitching together pieces
rlm@451 2428 from previous experience. It prefers longer chains of previous
rlm@451 2429 experience to shorter ones. For example, during training the worm
rlm@451 2430 might rest on the ground for one second before it performs its
rlm@451 2431 excercises. If during recognition the worm rests on the ground for
rlm@451 2432 five seconds, =longest-thread= will accomodate this five second
rlm@451 2433 rest period by looping the one second rest chain five times.
rlm@451 2434
rlm@451 2435 =longest-thread= takes time proportinal to the average number of
rlm@451 2436 entries in a proprioceptive bin, because for each element in the
rlm@451 2437 starting bin it performes a series of set lookups in the preceeding
rlm@451 2438 bins. If the total history is limited, then this is only a constant
rlm@451 2439 multiple times the number of entries in the starting bin. This
rlm@451 2440 analysis also applies even if the action requires multiple longest
rlm@451 2441 chains -- it's still the average number of entries in a
rlm@451 2442 proprioceptive bin times the desired chain length. Because
rlm@451 2443 =longest-thread= is so efficient and simple, I can interpret
rlm@451 2444 worm-actions in real time.
rlm@449 2445
rlm@450 2446 #+caption: Program to calculate empathy by tracing though \Phi-space
rlm@450 2447 #+caption: and finding the longest (ie. most coherent) interpretation
rlm@450 2448 #+caption: of the data.
rlm@450 2449 #+name: longest-thread
rlm@452 2450 #+attr_latex: [htpb]
rlm@452 2451 #+begin_listing clojure
rlm@450 2452 #+begin_src clojure
rlm@449 2453 (defn longest-thread
rlm@449 2454 "Find the longest thread from phi-index-sets. The index sets should
rlm@449 2455 be ordered from most recent to least recent."
rlm@449 2456 [phi-index-sets]
rlm@449 2457 (loop [result '()
rlm@449 2458 [thread-bases & remaining :as phi-index-sets] phi-index-sets]
rlm@449 2459 (if (empty? phi-index-sets)
rlm@449 2460 (vec result)
rlm@449 2461 (let [threads
rlm@449 2462 (for [thread-base thread-bases]
rlm@449 2463 (loop [thread (list thread-base)
rlm@449 2464 remaining remaining]
rlm@449 2465 (let [next-index (dec (first thread))]
rlm@449 2466 (cond (empty? remaining) thread
rlm@449 2467 (contains? (first remaining) next-index)
rlm@449 2468 (recur
rlm@449 2469 (cons next-index thread) (rest remaining))
rlm@449 2470 :else thread))))
rlm@449 2471 longest-thread
rlm@449 2472 (reduce (fn [thread-a thread-b]
rlm@449 2473 (if (> (count thread-a) (count thread-b))
rlm@449 2474 thread-a thread-b))
rlm@449 2475 '(nil)
rlm@449 2476 threads)]
rlm@449 2477 (recur (concat longest-thread result)
rlm@449 2478 (drop (count longest-thread) phi-index-sets))))))
rlm@450 2479 #+end_src
rlm@450 2480 #+end_listing
rlm@450 2481
rlm@451 2482 There is one final piece, which is to replace missing sensory data
rlm@451 2483 with a best-guess estimate. While I could fill in missing data by
rlm@451 2484 using a gradient over the closest known sensory data points,
rlm@451 2485 averages can be misleading. It is certainly possible to create an
rlm@451 2486 impossible sensory state by averaging two possible sensory states.
rlm@451 2487 Therefore, I simply replicate the most recent sensory experience to
rlm@451 2488 fill in the gaps.
rlm@449 2489
rlm@449 2490 #+caption: Fill in blanks in sensory experience by replicating the most
rlm@449 2491 #+caption: recent experience.
rlm@449 2492 #+name: infer-nils
rlm@452 2493 #+attr_latex: [htpb]
rlm@452 2494 #+begin_listing clojure
rlm@449 2495 #+begin_src clojure
rlm@449 2496 (defn infer-nils
rlm@449 2497 "Replace nils with the next available non-nil element in the
rlm@449 2498 sequence, or barring that, 0."
rlm@449 2499 [s]
rlm@449 2500 (loop [i (dec (count s))
rlm@449 2501 v (transient s)]
rlm@449 2502 (if (zero? i) (persistent! v)
rlm@449 2503 (if-let [cur (v i)]
rlm@449 2504 (if (get v (dec i) 0)
rlm@449 2505 (recur (dec i) v)
rlm@449 2506 (recur (dec i) (assoc! v (dec i) cur)))
rlm@449 2507 (recur i (assoc! v i 0))))))
rlm@449 2508 #+end_src
rlm@449 2509 #+end_listing
rlm@435 2510
rlm@441 2511 ** Efficient action recognition with =EMPATH=
rlm@451 2512
rlm@451 2513 To use =EMPATH= with the worm, I first need to gather a set of
rlm@451 2514 experiences from the worm that includes the actions I want to
rlm@452 2515 recognize. The =generate-phi-space= program (listing
rlm@451 2516 \ref{generate-phi-space} runs the worm through a series of
rlm@451 2517 exercices and gatheres those experiences into a vector. The
rlm@451 2518 =do-all-the-things= program is a routine expressed in a simple
rlm@452 2519 muscle contraction script language for automated worm control. It
rlm@452 2520 causes the worm to rest, curl, and wiggle over about 700 frames
rlm@452 2521 (approx. 11 seconds).
rlm@425 2522
rlm@451 2523 #+caption: Program to gather the worm's experiences into a vector for
rlm@451 2524 #+caption: further processing. The =motor-control-program= line uses
rlm@451 2525 #+caption: a motor control script that causes the worm to execute a series
rlm@451 2526 #+caption: of ``exercices'' that include all the action predicates.
rlm@451 2527 #+name: generate-phi-space
rlm@452 2528 #+attr_latex: [htpb]
rlm@452 2529 #+begin_listing clojure
rlm@451 2530 #+begin_src clojure
rlm@451 2531 (def do-all-the-things
rlm@451 2532 (concat
rlm@451 2533 curl-script
rlm@451 2534 [[300 :d-ex 40]
rlm@451 2535 [320 :d-ex 0]]
rlm@451 2536 (shift-script 280 (take 16 wiggle-script))))
rlm@451 2537
rlm@451 2538 (defn generate-phi-space []
rlm@451 2539 (let [experiences (atom [])]
rlm@451 2540 (run-world
rlm@451 2541 (apply-map
rlm@451 2542 worm-world
rlm@451 2543 (merge
rlm@451 2544 (worm-world-defaults)
rlm@451 2545 {:end-frame 700
rlm@451 2546 :motor-control
rlm@451 2547 (motor-control-program worm-muscle-labels do-all-the-things)
rlm@451 2548 :experiences experiences})))
rlm@451 2549 @experiences))
rlm@451 2550 #+end_src
rlm@451 2551 #+end_listing
rlm@451 2552
rlm@451 2553 #+caption: Use longest thread and a phi-space generated from a short
rlm@451 2554 #+caption: exercise routine to interpret actions during free play.
rlm@451 2555 #+name: empathy-debug
rlm@452 2556 #+attr_latex: [htpb]
rlm@452 2557 #+begin_listing clojure
rlm@451 2558 #+begin_src clojure
rlm@451 2559 (defn init []
rlm@451 2560 (def phi-space (generate-phi-space))
rlm@451 2561 (def phi-scan (gen-phi-scan phi-space)))
rlm@451 2562
rlm@451 2563 (defn empathy-demonstration []
rlm@451 2564 (let [proprio (atom ())]
rlm@451 2565 (fn
rlm@451 2566 [experiences text]
rlm@451 2567 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451 2568 (swap! proprio (partial cons phi-indices))
rlm@451 2569 (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451 2570 empathy (mapv phi-space (infer-nils exp-thread))]
rlm@451 2571 (println-repl (vector:last-n exp-thread 22))
rlm@451 2572 (cond
rlm@451 2573 (grand-circle? empathy) (.setText text "Grand Circle")
rlm@451 2574 (curled? empathy) (.setText text "Curled")
rlm@451 2575 (wiggling? empathy) (.setText text "Wiggling")
rlm@451 2576 (resting? empathy) (.setText text "Resting")
rlm@451 2577 :else (.setText text "Unknown")))))))
rlm@451 2578
rlm@451 2579 (defn empathy-experiment [record]
rlm@451 2580 (.start (worm-world :experience-watch (debug-experience-phi)
rlm@451 2581 :record record :worm worm*)))
rlm@451 2582 #+end_src
rlm@451 2583 #+end_listing
rlm@451 2584
rlm@451 2585 The result of running =empathy-experiment= is that the system is
rlm@451 2586 generally able to interpret worm actions using the action-predicates
rlm@451 2587 on simulated sensory data just as well as with actual data. Figure
rlm@451 2588 \ref{empathy-debug-image} was generated using =empathy-experiment=:
rlm@451 2589
rlm@451 2590 #+caption: From only proprioceptive data, =EMPATH= was able to infer
rlm@451 2591 #+caption: the complete sensory experience and classify four poses
rlm@451 2592 #+caption: (The last panel shows a composite image of \emph{wriggling},
rlm@451 2593 #+caption: a dynamic pose.)
rlm@451 2594 #+name: empathy-debug-image
rlm@451 2595 #+ATTR_LaTeX: :width 10cm :placement [H]
rlm@451 2596 [[./images/empathy-1.png]]
rlm@451 2597
rlm@451 2598 One way to measure the performance of =EMPATH= is to compare the
rlm@451 2599 sutiability of the imagined sense experience to trigger the same
rlm@451 2600 action predicates as the real sensory experience.
rlm@451 2601
rlm@451 2602 #+caption: Determine how closely empathy approximates actual
rlm@451 2603 #+caption: sensory data.
rlm@451 2604 #+name: test-empathy-accuracy
rlm@452 2605 #+attr_latex: [htpb]
rlm@452 2606 #+begin_listing clojure
rlm@451 2607 #+begin_src clojure
rlm@451 2608 (def worm-action-label
rlm@451 2609 (juxt grand-circle? curled? wiggling?))
rlm@451 2610
rlm@451 2611 (defn compare-empathy-with-baseline [matches]
rlm@451 2612 (let [proprio (atom ())]
rlm@451 2613 (fn
rlm@451 2614 [experiences text]
rlm@451 2615 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
rlm@451 2616 (swap! proprio (partial cons phi-indices))
rlm@451 2617 (let [exp-thread (longest-thread (take 300 @proprio))
rlm@451 2618 empathy (mapv phi-space (infer-nils exp-thread))
rlm@451 2619 experience-matches-empathy
rlm@451 2620 (= (worm-action-label experiences)
rlm@451 2621 (worm-action-label empathy))]
rlm@451 2622 (println-repl experience-matches-empathy)
rlm@451 2623 (swap! matches #(conj % experience-matches-empathy)))))))
rlm@451 2624
rlm@451 2625 (defn accuracy [v]
rlm@451 2626 (float (/ (count (filter true? v)) (count v))))
rlm@451 2627
rlm@451 2628 (defn test-empathy-accuracy []
rlm@451 2629 (let [res (atom [])]
rlm@451 2630 (run-world
rlm@451 2631 (worm-world :experience-watch
rlm@451 2632 (compare-empathy-with-baseline res)
rlm@451 2633 :worm worm*))
rlm@451 2634 (accuracy @res)))
rlm@451 2635 #+end_src
rlm@451 2636 #+end_listing
rlm@451 2637
rlm@451 2638 Running =test-empathy-accuracy= using the very short exercise
rlm@451 2639 program defined in listing \ref{generate-phi-space}, and then doing
rlm@451 2640 a similar pattern of activity manually yeilds an accuracy of around
rlm@451 2641 73%. This is based on very limited worm experience. By training the
rlm@451 2642 worm for longer, the accuracy dramatically improves.
rlm@451 2643
rlm@451 2644 #+caption: Program to generate \Phi-space using manual training.
rlm@451 2645 #+name: manual-phi-space
rlm@452 2646 #+attr_latex: [htpb]
rlm@451 2647 #+begin_listing clojure
rlm@451 2648 #+begin_src clojure
rlm@451 2649 (defn init-interactive []
rlm@451 2650 (def phi-space
rlm@451 2651 (let [experiences (atom [])]
rlm@451 2652 (run-world
rlm@451 2653 (apply-map
rlm@451 2654 worm-world
rlm@451 2655 (merge
rlm@451 2656 (worm-world-defaults)
rlm@451 2657 {:experiences experiences})))
rlm@451 2658 @experiences))
rlm@451 2659 (def phi-scan (gen-phi-scan phi-space)))
rlm@451 2660 #+end_src
rlm@451 2661 #+end_listing
rlm@451 2662
rlm@451 2663 After about 1 minute of manual training, I was able to achieve 95%
rlm@451 2664 accuracy on manual testing of the worm using =init-interactive= and
rlm@452 2665 =test-empathy-accuracy=. The majority of errors are near the
rlm@452 2666 boundaries of transitioning from one type of action to another.
rlm@452 2667 During these transitions the exact label for the action is more open
rlm@452 2668 to interpretation, and dissaggrement between empathy and experience
rlm@452 2669 is more excusable.
rlm@450 2670
rlm@449 2671 ** Digression: bootstrapping touch using free exploration
rlm@449 2672
rlm@452 2673 In the previous section I showed how to compute actions in terms of
rlm@452 2674 body-centered predicates which relied averate touch activation of
rlm@452 2675 pre-defined regions of the worm's skin. What if, instead of recieving
rlm@452 2676 touch pre-grouped into the six faces of each worm segment, the true
rlm@452 2677 topology of the worm's skin was unknown? This is more similiar to how
rlm@452 2678 a nerve fiber bundle might be arranged. While two fibers that are
rlm@452 2679 close in a nerve bundle /might/ correspond to two touch sensors that
rlm@452 2680 are close together on the skin, the process of taking a complicated
rlm@452 2681 surface and forcing it into essentially a circle requires some cuts
rlm@452 2682 and rerragenments.
rlm@452 2683
rlm@452 2684 In this section I show how to automatically learn the skin-topology of
rlm@452 2685 a worm segment by free exploration. As the worm rolls around on the
rlm@452 2686 floor, large sections of its surface get activated. If the worm has
rlm@452 2687 stopped moving, then whatever region of skin that is touching the
rlm@452 2688 floor is probably an important region, and should be recorded.
rlm@452 2689
rlm@452 2690 #+caption: Program to detect whether the worm is in a resting state
rlm@452 2691 #+caption: with one face touching the floor.
rlm@452 2692 #+name: pure-touch
rlm@452 2693 #+begin_listing clojure
rlm@452 2694 #+begin_src clojure
rlm@452 2695 (def full-contact [(float 0.0) (float 0.1)])
rlm@452 2696
rlm@452 2697 (defn pure-touch?
rlm@452 2698 "This is worm specific code to determine if a large region of touch
rlm@452 2699 sensors is either all on or all off."
rlm@452 2700 [[coords touch :as touch-data]]
rlm@452 2701 (= (set (map first touch)) (set full-contact)))
rlm@452 2702 #+end_src
rlm@452 2703 #+end_listing
rlm@452 2704
rlm@452 2705 After collecting these important regions, there will many nearly
rlm@452 2706 similiar touch regions. While for some purposes the subtle
rlm@452 2707 differences between these regions will be important, for my
rlm@452 2708 purposes I colapse them into mostly non-overlapping sets using
rlm@452 2709 =remove-similiar= in listing \ref{remove-similiar}
rlm@452 2710
rlm@452 2711 #+caption: Program to take a lits of set of points and ``collapse them''
rlm@452 2712 #+caption: so that the remaining sets in the list are siginificantly
rlm@452 2713 #+caption: different from each other. Prefer smaller sets to larger ones.
rlm@452 2714 #+name: remove-similiar
rlm@452 2715 #+begin_listing clojure
rlm@452 2716 #+begin_src clojure
rlm@452 2717 (defn remove-similar
rlm@452 2718 [coll]
rlm@452 2719 (loop [result () coll (sort-by (comp - count) coll)]
rlm@452 2720 (if (empty? coll) result
rlm@452 2721 (let [[x & xs] coll
rlm@452 2722 c (count x)]
rlm@452 2723 (if (some
rlm@452 2724 (fn [other-set]
rlm@452 2725 (let [oc (count other-set)]
rlm@452 2726 (< (- (count (union other-set x)) c) (* oc 0.1))))
rlm@452 2727 xs)
rlm@452 2728 (recur result xs)
rlm@452 2729 (recur (cons x result) xs))))))
rlm@452 2730 #+end_src
rlm@452 2731 #+end_listing
rlm@452 2732
rlm@452 2733 Actually running this simulation is easy given =CORTEX='s facilities.
rlm@452 2734
rlm@452 2735 #+caption: Collect experiences while the worm moves around. Filter the touch
rlm@452 2736 #+caption: sensations by stable ones, collapse similiar ones together,
rlm@452 2737 #+caption: and report the regions learned.
rlm@452 2738 #+name: learn-touch
rlm@452 2739 #+begin_listing clojure
rlm@452 2740 #+begin_src clojure
rlm@452 2741 (defn learn-touch-regions []
rlm@452 2742 (let [experiences (atom [])
rlm@452 2743 world (apply-map
rlm@452 2744 worm-world
rlm@452 2745 (assoc (worm-segment-defaults)
rlm@452 2746 :experiences experiences))]
rlm@452 2747 (run-world world)
rlm@452 2748 (->>
rlm@452 2749 @experiences
rlm@452 2750 (drop 175)
rlm@452 2751 ;; access the single segment's touch data
rlm@452 2752 (map (comp first :touch))
rlm@452 2753 ;; only deal with "pure" touch data to determine surfaces
rlm@452 2754 (filter pure-touch?)
rlm@452 2755 ;; associate coordinates with touch values
rlm@452 2756 (map (partial apply zipmap))
rlm@452 2757 ;; select those regions where contact is being made
rlm@452 2758 (map (partial group-by second))
rlm@452 2759 (map #(get % full-contact))
rlm@452 2760 (map (partial map first))
rlm@452 2761 ;; remove redundant/subset regions
rlm@452 2762 (map set)
rlm@452 2763 remove-similar)))
rlm@452 2764
rlm@452 2765 (defn learn-and-view-touch-regions []
rlm@452 2766 (map view-touch-region
rlm@452 2767 (learn-touch-regions)))
rlm@452 2768 #+end_src
rlm@452 2769 #+end_listing
rlm@452 2770
rlm@452 2771 The only thing remining to define is the particular motion the worm
rlm@452 2772 must take. I accomplish this with a simple motor control program.
rlm@452 2773
rlm@452 2774 #+caption: Motor control program for making the worm roll on the ground.
rlm@452 2775 #+caption: This could also be replaced with random motion.
rlm@452 2776 #+name: worm-roll
rlm@452 2777 #+begin_listing clojure
rlm@452 2778 #+begin_src clojure
rlm@452 2779 (defn touch-kinesthetics []
rlm@452 2780 [[170 :lift-1 40]
rlm@452 2781 [190 :lift-1 19]
rlm@452 2782 [206 :lift-1 0]
rlm@452 2783
rlm@452 2784 [400 :lift-2 40]
rlm@452 2785 [410 :lift-2 0]
rlm@452 2786
rlm@452 2787 [570 :lift-2 40]
rlm@452 2788 [590 :lift-2 21]
rlm@452 2789 [606 :lift-2 0]
rlm@452 2790
rlm@452 2791 [800 :lift-1 30]
rlm@452 2792 [809 :lift-1 0]
rlm@452 2793
rlm@452 2794 [900 :roll-2 40]
rlm@452 2795 [905 :roll-2 20]
rlm@452 2796 [910 :roll-2 0]
rlm@452 2797
rlm@452 2798 [1000 :roll-2 40]
rlm@452 2799 [1005 :roll-2 20]
rlm@452 2800 [1010 :roll-2 0]
rlm@452 2801
rlm@452 2802 [1100 :roll-2 40]
rlm@452 2803 [1105 :roll-2 20]
rlm@452 2804 [1110 :roll-2 0]
rlm@452 2805 ])
rlm@452 2806 #+end_src
rlm@452 2807 #+end_listing
rlm@452 2808
rlm@452 2809
rlm@452 2810 #+caption: The small worm rolls around on the floor, driven
rlm@452 2811 #+caption: by the motor control program in listing \ref{worm-roll}.
rlm@452 2812 #+name: worm-roll
rlm@452 2813 #+ATTR_LaTeX: :width 12cm
rlm@452 2814 [[./images/worm-roll.png]]
rlm@452 2815
rlm@452 2816
rlm@452 2817 #+caption: After completing its adventures, the worm now knows
rlm@452 2818 #+caption: how its touch sensors are arranged along its skin. These
rlm@452 2819 #+caption: are the regions that were deemed important by
rlm@452 2820 #+caption: =learn-touch-regions=. Note that the worm has discovered
rlm@452 2821 #+caption: that it has six sides.
rlm@452 2822 #+name: worm-touch-map
rlm@452 2823 #+ATTR_LaTeX: :width 12cm
rlm@452 2824 [[./images/touch-learn.png]]
rlm@452 2825
rlm@452 2826 While simple, =learn-touch-regions= exploits regularities in both
rlm@452 2827 the worm's physiology and the worm's environment to correctly
rlm@452 2828 deduce that the worm has six sides. Note that =learn-touch-regions=
rlm@452 2829 would work just as well even if the worm's touch sense data were
rlm@452 2830 completely scrambled. The cross shape is just for convienence. This
rlm@452 2831 example justifies the use of pre-defined touch regions in =EMPATH=.
rlm@452 2832
rlm@465 2833 * COMMENT Contributions
rlm@454 2834
rlm@461 2835 In this thesis you have seen the =CORTEX= system, a complete
rlm@461 2836 environment for creating simulated creatures. You have seen how to
rlm@461 2837 implement five senses including touch, proprioception, hearing,
rlm@461 2838 vision, and muscle tension. You have seen how to create new creatues
rlm@461 2839 using blender, a 3D modeling tool. I hope that =CORTEX= will be
rlm@461 2840 useful in further research projects. To this end I have included the
rlm@461 2841 full source to =CORTEX= along with a large suite of tests and
rlm@461 2842 examples. I have also created a user guide for =CORTEX= which is
rlm@461 2843 inculded in an appendix to this thesis.
rlm@447 2844
rlm@461 2845 You have also seen how I used =CORTEX= as a platform to attach the
rlm@461 2846 /action recognition/ problem, which is the problem of recognizing
rlm@461 2847 actions in video. You saw a simple system called =EMPATH= which
rlm@461 2848 ientifies actions by first describing actions in a body-centerd,
rlm@461 2849 rich sense language, then infering a full range of sensory
rlm@461 2850 experience from limited data using previous experience gained from
rlm@461 2851 free play.
rlm@447 2852
rlm@461 2853 As a minor digression, you also saw how I used =CORTEX= to enable a
rlm@461 2854 tiny worm to discover the topology of its skin simply by rolling on
rlm@461 2855 the ground.
rlm@461 2856
rlm@461 2857 In conclusion, the main contributions of this thesis are:
rlm@461 2858
rlm@461 2859 - =CORTEX=, a system for creating simulated creatures with rich
rlm@461 2860 senses.
rlm@461 2861 - =EMPATH=, a program for recognizing actions by imagining sensory
rlm@461 2862 experience.
rlm@447 2863
rlm@447 2864 # An anatomical joke:
rlm@447 2865 # - Training
rlm@447 2866 # - Skeletal imitation
rlm@447 2867 # - Sensory fleshing-out
rlm@447 2868 # - Classification