view thesis/cortex.org @ 453:6db37c4aa1ee

save alt-worm files.
author Robert McIntyre <rlm@mit.edu>
date Wed, 26 Mar 2014 22:19:33 -0400
parents f339e3d5cc8c
children 26c13c42481f
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1 #+title: =CORTEX=
2 #+author: Robert McIntyre
3 #+email: rlm@mit.edu
4 #+description: Using embodied AI to facilitate Artificial Imagination.
5 #+keywords: AI, clojure, embodiment
6 #+LaTeX_CLASS_OPTIONS: [nofloat]
8 * Empathy and Embodiment as problem solving strategies
10 By the end of this thesis, you will have seen a novel approach to
11 interpreting video using embodiment and empathy. You will have also
12 seen one way to efficiently implement empathy for embodied
13 creatures. Finally, you will become familiar with =CORTEX=, a system
14 for designing and simulating creatures with rich senses, which you
15 may choose to use in your own research.
17 This is the core vision of my thesis: That one of the important ways
18 in which we understand others is by imagining ourselves in their
19 position and emphatically feeling experiences relative to our own
20 bodies. By understanding events in terms of our own previous
21 corporeal experience, we greatly constrain the possibilities of what
22 would otherwise be an unwieldy exponential search. This extra
23 constraint can be the difference between easily understanding what
24 is happening in a video and being completely lost in a sea of
25 incomprehensible color and movement.
27 ** Recognizing actions in video is extremely difficult
29 Consider for example the problem of determining what is happening
30 in a video of which this is one frame:
32 #+caption: A cat drinking some water. Identifying this action is
33 #+caption: beyond the state of the art for computers.
34 #+ATTR_LaTeX: :width 7cm
35 [[./images/cat-drinking.jpg]]
37 It is currently impossible for any computer program to reliably
38 label such a video as ``drinking''. And rightly so -- it is a very
39 hard problem! What features can you describe in terms of low level
40 functions of pixels that can even begin to describe at a high level
41 what is happening here?
43 Or suppose that you are building a program that recognizes chairs.
44 How could you ``see'' the chair in figure \ref{hidden-chair}?
46 #+caption: The chair in this image is quite obvious to humans, but I
47 #+caption: doubt that any modern computer vision program can find it.
48 #+name: hidden-chair
49 #+ATTR_LaTeX: :width 10cm
50 [[./images/fat-person-sitting-at-desk.jpg]]
52 Finally, how is it that you can easily tell the difference between
53 how the girls /muscles/ are working in figure \ref{girl}?
55 #+caption: The mysterious ``common sense'' appears here as you are able
56 #+caption: to discern the difference in how the girl's arm muscles
57 #+caption: are activated between the two images.
58 #+name: girl
59 #+ATTR_LaTeX: :width 7cm
60 [[./images/wall-push.png]]
62 Each of these examples tells us something about what might be going
63 on in our minds as we easily solve these recognition problems.
65 The hidden chairs show us that we are strongly triggered by cues
66 relating to the position of human bodies, and that we can determine
67 the overall physical configuration of a human body even if much of
68 that body is occluded.
70 The picture of the girl pushing against the wall tells us that we
71 have common sense knowledge about the kinetics of our own bodies.
72 We know well how our muscles would have to work to maintain us in
73 most positions, and we can easily project this self-knowledge to
74 imagined positions triggered by images of the human body.
76 ** =EMPATH= neatly solves recognition problems
78 I propose a system that can express the types of recognition
79 problems above in a form amenable to computation. It is split into
80 four parts:
82 - Free/Guided Play :: The creature moves around and experiences the
83 world through its unique perspective. Many otherwise
84 complicated actions are easily described in the language of a
85 full suite of body-centered, rich senses. For example,
86 drinking is the feeling of water sliding down your throat, and
87 cooling your insides. It's often accompanied by bringing your
88 hand close to your face, or bringing your face close to water.
89 Sitting down is the feeling of bending your knees, activating
90 your quadriceps, then feeling a surface with your bottom and
91 relaxing your legs. These body-centered action descriptions
92 can be either learned or hard coded.
93 - Posture Imitation :: When trying to interpret a video or image,
94 the creature takes a model of itself and aligns it with
95 whatever it sees. This alignment can even cross species, as
96 when humans try to align themselves with things like ponies,
97 dogs, or other humans with a different body type.
98 - Empathy :: The alignment triggers associations with
99 sensory data from prior experiences. For example, the
100 alignment itself easily maps to proprioceptive data. Any
101 sounds or obvious skin contact in the video can to a lesser
102 extent trigger previous experience. Segments of previous
103 experiences are stitched together to form a coherent and
104 complete sensory portrait of the scene.
105 - Recognition :: With the scene described in terms of first
106 person sensory events, the creature can now run its
107 action-identification programs on this synthesized sensory
108 data, just as it would if it were actually experiencing the
109 scene first-hand. If previous experience has been accurately
110 retrieved, and if it is analogous enough to the scene, then
111 the creature will correctly identify the action in the scene.
113 For example, I think humans are able to label the cat video as
114 ``drinking'' because they imagine /themselves/ as the cat, and
115 imagine putting their face up against a stream of water and
116 sticking out their tongue. In that imagined world, they can feel
117 the cool water hitting their tongue, and feel the water entering
118 their body, and are able to recognize that /feeling/ as drinking.
119 So, the label of the action is not really in the pixels of the
120 image, but is found clearly in a simulation inspired by those
121 pixels. An imaginative system, having been trained on drinking and
122 non-drinking examples and learning that the most important
123 component of drinking is the feeling of water sliding down one's
124 throat, would analyze a video of a cat drinking in the following
125 manner:
127 1. Create a physical model of the video by putting a ``fuzzy''
128 model of its own body in place of the cat. Possibly also create
129 a simulation of the stream of water.
131 2. Play out this simulated scene and generate imagined sensory
132 experience. This will include relevant muscle contractions, a
133 close up view of the stream from the cat's perspective, and most
134 importantly, the imagined feeling of water entering the
135 mouth. The imagined sensory experience can come from a
136 simulation of the event, but can also be pattern-matched from
137 previous, similar embodied experience.
139 3. The action is now easily identified as drinking by the sense of
140 taste alone. The other senses (such as the tongue moving in and
141 out) help to give plausibility to the simulated action. Note that
142 the sense of vision, while critical in creating the simulation,
143 is not critical for identifying the action from the simulation.
145 For the chair examples, the process is even easier:
147 1. Align a model of your body to the person in the image.
149 2. Generate proprioceptive sensory data from this alignment.
151 3. Use the imagined proprioceptive data as a key to lookup related
152 sensory experience associated with that particular proproceptive
153 feeling.
155 4. Retrieve the feeling of your bottom resting on a surface, your
156 knees bent, and your leg muscles relaxed.
158 5. This sensory information is consistent with the =sitting?=
159 sensory predicate, so you (and the entity in the image) must be
160 sitting.
162 6. There must be a chair-like object since you are sitting.
164 Empathy offers yet another alternative to the age-old AI
165 representation question: ``What is a chair?'' --- A chair is the
166 feeling of sitting.
168 My program, =EMPATH= uses this empathic problem solving technique
169 to interpret the actions of a simple, worm-like creature.
171 #+caption: The worm performs many actions during free play such as
172 #+caption: curling, wiggling, and resting.
173 #+name: worm-intro
174 #+ATTR_LaTeX: :width 15cm
175 [[./images/worm-intro-white.png]]
177 #+caption: =EMPATH= recognized and classified each of these poses by
178 #+caption: inferring the complete sensory experience from
179 #+caption: proprioceptive data.
180 #+name: worm-recognition-intro
181 #+ATTR_LaTeX: :width 15cm
182 [[./images/worm-poses.png]]
184 One powerful advantage of empathic problem solving is that it
185 factors the action recognition problem into two easier problems. To
186 use empathy, you need an /aligner/, which takes the video and a
187 model of your body, and aligns the model with the video. Then, you
188 need a /recognizer/, which uses the aligned model to interpret the
189 action. The power in this method lies in the fact that you describe
190 all actions form a body-centered viewpoint. You are less tied to
191 the particulars of any visual representation of the actions. If you
192 teach the system what ``running'' is, and you have a good enough
193 aligner, the system will from then on be able to recognize running
194 from any point of view, even strange points of view like above or
195 underneath the runner. This is in contrast to action recognition
196 schemes that try to identify actions using a non-embodied approach.
197 If these systems learn about running as viewed from the side, they
198 will not automatically be able to recognize running from any other
199 viewpoint.
201 Another powerful advantage is that using the language of multiple
202 body-centered rich senses to describe body-centerd actions offers a
203 massive boost in descriptive capability. Consider how difficult it
204 would be to compose a set of HOG filters to describe the action of
205 a simple worm-creature ``curling'' so that its head touches its
206 tail, and then behold the simplicity of describing thus action in a
207 language designed for the task (listing \ref{grand-circle-intro}):
209 #+caption: Body-centerd actions are best expressed in a body-centered
210 #+caption: language. This code detects when the worm has curled into a
211 #+caption: full circle. Imagine how you would replicate this functionality
212 #+caption: using low-level pixel features such as HOG filters!
213 #+name: grand-circle-intro
214 #+attr_latex: [htpb]
215 #+begin_listing clojure
216 #+begin_src clojure
217 (defn grand-circle?
218 "Does the worm form a majestic circle (one end touching the other)?"
219 [experiences]
220 (and (curled? experiences)
221 (let [worm-touch (:touch (peek experiences))
222 tail-touch (worm-touch 0)
223 head-touch (worm-touch 4)]
224 (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
225 (< 0.55 (contact worm-segment-top-tip head-touch))))))
226 #+end_src
227 #+end_listing
230 ** =CORTEX= is a toolkit for building sensate creatures
232 I built =CORTEX= to be a general AI research platform for doing
233 experiments involving multiple rich senses and a wide variety and
234 number of creatures. I intend it to be useful as a library for many
235 more projects than just this one. =CORTEX= was necessary to meet a
236 need among AI researchers at CSAIL and beyond, which is that people
237 often will invent neat ideas that are best expressed in the
238 language of creatures and senses, but in order to explore those
239 ideas they must first build a platform in which they can create
240 simulated creatures with rich senses! There are many ideas that
241 would be simple to execute (such as =EMPATH=), but attached to them
242 is the multi-month effort to make a good creature simulator. Often,
243 that initial investment of time proves to be too much, and the
244 project must make do with a lesser environment.
246 =CORTEX= is well suited as an environment for embodied AI research
247 for three reasons:
249 - You can create new creatures using Blender, a popular 3D modeling
250 program. Each sense can be specified using special blender nodes
251 with biologically inspired paramaters. You need not write any
252 code to create a creature, and can use a wide library of
253 pre-existing blender models as a base for your own creatures.
255 - =CORTEX= implements a wide variety of senses, including touch,
256 proprioception, vision, hearing, and muscle tension. Complicated
257 senses like touch, and vision involve multiple sensory elements
258 embedded in a 2D surface. You have complete control over the
259 distribution of these sensor elements through the use of simple
260 png image files. In particular, =CORTEX= implements more
261 comprehensive hearing than any other creature simulation system
262 available.
264 - =CORTEX= supports any number of creatures and any number of
265 senses. Time in =CORTEX= dialates so that the simulated creatures
266 always precieve a perfectly smooth flow of time, regardless of
267 the actual computational load.
269 =CORTEX= is built on top of =jMonkeyEngine3=, which is a video game
270 engine designed to create cross-platform 3D desktop games. =CORTEX=
271 is mainly written in clojure, a dialect of =LISP= that runs on the
272 java virtual machine (JVM). The API for creating and simulating
273 creatures and senses is entirely expressed in clojure, though many
274 senses are implemented at the layer of jMonkeyEngine or below. For
275 example, for the sense of hearing I use a layer of clojure code on
276 top of a layer of java JNI bindings that drive a layer of =C++=
277 code which implements a modified version of =OpenAL= to support
278 multiple listeners. =CORTEX= is the only simulation environment
279 that I know of that can support multiple entities that can each
280 hear the world from their own perspective. Other senses also
281 require a small layer of Java code. =CORTEX= also uses =bullet=, a
282 physics simulator written in =C=.
284 #+caption: Here is the worm from above modeled in Blender, a free
285 #+caption: 3D-modeling program. Senses and joints are described
286 #+caption: using special nodes in Blender.
287 #+name: worm-recognition-intro
288 #+ATTR_LaTeX: :width 12cm
289 [[./images/blender-worm.png]]
291 Here are some thing I anticipate that =CORTEX= might be used for:
293 - exploring new ideas about sensory integration
294 - distributed communication among swarm creatures
295 - self-learning using free exploration,
296 - evolutionary algorithms involving creature construction
297 - exploration of exoitic senses and effectors that are not possible
298 in the real world (such as telekenisis or a semantic sense)
299 - imagination using subworlds
301 During one test with =CORTEX=, I created 3,000 creatures each with
302 their own independent senses and ran them all at only 1/80 real
303 time. In another test, I created a detailed model of my own hand,
304 equipped with a realistic distribution of touch (more sensitive at
305 the fingertips), as well as eyes and ears, and it ran at around 1/4
306 real time.
308 #+BEGIN_LaTeX
309 \begin{sidewaysfigure}
310 \includegraphics[width=9.5in]{images/full-hand.png}
311 \caption{
312 I modeled my own right hand in Blender and rigged it with all the
313 senses that {\tt CORTEX} supports. My simulated hand has a
314 biologically inspired distribution of touch sensors. The senses are
315 displayed on the right, and the simulation is displayed on the
316 left. Notice that my hand is curling its fingers, that it can see
317 its own finger from the eye in its palm, and that it can feel its
318 own thumb touching its palm.}
319 \end{sidewaysfigure}
320 #+END_LaTeX
322 ** Contributions
324 - I built =CORTEX=, a comprehensive platform for embodied AI
325 experiments. =CORTEX= supports many features lacking in other
326 systems, such proper simulation of hearing. It is easy to create
327 new =CORTEX= creatures using Blender, a free 3D modeling program.
329 - I built =EMPATH=, which uses =CORTEX= to identify the actions of
330 a worm-like creature using a computational model of empathy.
332 * Building =CORTEX=
334 ** To explore embodiment, we need a world, body, and senses
336 ** Because of Time, simulation is perferable to reality
338 ** Video game engines are a great starting point
340 ** Bodies are composed of segments connected by joints
342 ** Eyes reuse standard video game components
344 ** Hearing is hard; =CORTEX= does it right
346 ** Touch uses hundreds of hair-like elements
348 ** Proprioception is the sense that makes everything ``real''
350 ** Muscles are both effectors and sensors
352 ** =CORTEX= brings complex creatures to life!
354 ** =CORTEX= enables many possiblities for further research
356 * Empathy in a simulated worm
358 Here I develop a computational model of empathy, using =CORTEX= as a
359 base. Empathy in this context is the ability to observe another
360 creature and infer what sorts of sensations that creature is
361 feeling. My empathy algorithm involves multiple phases. First is
362 free-play, where the creature moves around and gains sensory
363 experience. From this experience I construct a representation of the
364 creature's sensory state space, which I call \Phi-space. Using
365 \Phi-space, I construct an efficient function which takes the
366 limited data that comes from observing another creature and enriches
367 it full compliment of imagined sensory data. I can then use the
368 imagined sensory data to recognize what the observed creature is
369 doing and feeling, using straightforward embodied action predicates.
370 This is all demonstrated with using a simple worm-like creature, and
371 recognizing worm-actions based on limited data.
373 #+caption: Here is the worm with which we will be working.
374 #+caption: It is composed of 5 segments. Each segment has a
375 #+caption: pair of extensor and flexor muscles. Each of the
376 #+caption: worm's four joints is a hinge joint which allows
377 #+caption: about 30 degrees of rotation to either side. Each segment
378 #+caption: of the worm is touch-capable and has a uniform
379 #+caption: distribution of touch sensors on each of its faces.
380 #+caption: Each joint has a proprioceptive sense to detect
381 #+caption: relative positions. The worm segments are all the
382 #+caption: same except for the first one, which has a much
383 #+caption: higher weight than the others to allow for easy
384 #+caption: manual motor control.
385 #+name: basic-worm-view
386 #+ATTR_LaTeX: :width 10cm
387 [[./images/basic-worm-view.png]]
389 #+caption: Program for reading a worm from a blender file and
390 #+caption: outfitting it with the senses of proprioception,
391 #+caption: touch, and the ability to move, as specified in the
392 #+caption: blender file.
393 #+name: get-worm
394 #+begin_listing clojure
395 #+begin_src clojure
396 (defn worm []
397 (let [model (load-blender-model "Models/worm/worm.blend")]
398 {:body (doto model (body!))
399 :touch (touch! model)
400 :proprioception (proprioception! model)
401 :muscles (movement! model)}))
402 #+end_src
403 #+end_listing
405 ** Embodiment factors action recognition into managable parts
407 Using empathy, I divide the problem of action recognition into a
408 recognition process expressed in the language of a full compliment
409 of senses, and an imaganitive process that generates full sensory
410 data from partial sensory data. Splitting the action recognition
411 problem in this manner greatly reduces the total amount of work to
412 recognize actions: The imaganitive process is mostly just matching
413 previous experience, and the recognition process gets to use all
414 the senses to directly describe any action.
416 ** Action recognition is easy with a full gamut of senses
418 Embodied representations using multiple senses such as touch,
419 proprioception, and muscle tension turns out be be exceedingly
420 efficient at describing body-centered actions. It is the ``right
421 language for the job''. For example, it takes only around 5 lines
422 of LISP code to describe the action of ``curling'' using embodied
423 primitives. It takes about 10 lines to describe the seemingly
424 complicated action of wiggling.
426 The following action predicates each take a stream of sensory
427 experience, observe however much of it they desire, and decide
428 whether the worm is doing the action they describe. =curled?=
429 relies on proprioception, =resting?= relies on touch, =wiggling?=
430 relies on a fourier analysis of muscle contraction, and
431 =grand-circle?= relies on touch and reuses =curled?= as a gaurd.
433 #+caption: Program for detecting whether the worm is curled. This is the
434 #+caption: simplest action predicate, because it only uses the last frame
435 #+caption: of sensory experience, and only uses proprioceptive data. Even
436 #+caption: this simple predicate, however, is automatically frame
437 #+caption: independent and ignores vermopomorphic differences such as
438 #+caption: worm textures and colors.
439 #+name: curled
440 #+attr_latex: [htpb]
441 #+begin_listing clojure
442 #+begin_src clojure
443 (defn curled?
444 "Is the worm curled up?"
445 [experiences]
446 (every?
447 (fn [[_ _ bend]]
448 (> (Math/sin bend) 0.64))
449 (:proprioception (peek experiences))))
450 #+end_src
451 #+end_listing
453 #+caption: Program for summarizing the touch information in a patch
454 #+caption: of skin.
455 #+name: touch-summary
456 #+attr_latex: [htpb]
458 #+begin_listing clojure
459 #+begin_src clojure
460 (defn contact
461 "Determine how much contact a particular worm segment has with
462 other objects. Returns a value between 0 and 1, where 1 is full
463 contact and 0 is no contact."
464 [touch-region [coords contact :as touch]]
465 (-> (zipmap coords contact)
466 (select-keys touch-region)
467 (vals)
468 (#(map first %))
469 (average)
470 (* 10)
471 (- 1)
472 (Math/abs)))
473 #+end_src
474 #+end_listing
477 #+caption: Program for detecting whether the worm is at rest. This program
478 #+caption: uses a summary of the tactile information from the underbelly
479 #+caption: of the worm, and is only true if every segment is touching the
480 #+caption: floor. Note that this function contains no references to
481 #+caption: proprioction at all.
482 #+name: resting
483 #+attr_latex: [htpb]
484 #+begin_listing clojure
485 #+begin_src clojure
486 (def worm-segment-bottom (rect-region [8 15] [14 22]))
488 (defn resting?
489 "Is the worm resting on the ground?"
490 [experiences]
491 (every?
492 (fn [touch-data]
493 (< 0.9 (contact worm-segment-bottom touch-data)))
494 (:touch (peek experiences))))
495 #+end_src
496 #+end_listing
498 #+caption: Program for detecting whether the worm is curled up into a
499 #+caption: full circle. Here the embodied approach begins to shine, as
500 #+caption: I am able to both use a previous action predicate (=curled?=)
501 #+caption: as well as the direct tactile experience of the head and tail.
502 #+name: grand-circle
503 #+attr_latex: [htpb]
504 #+begin_listing clojure
505 #+begin_src clojure
506 (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))
508 (def worm-segment-top-tip (rect-region [0 15] [7 22]))
510 (defn grand-circle?
511 "Does the worm form a majestic circle (one end touching the other)?"
512 [experiences]
513 (and (curled? experiences)
514 (let [worm-touch (:touch (peek experiences))
515 tail-touch (worm-touch 0)
516 head-touch (worm-touch 4)]
517 (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
518 (< 0.55 (contact worm-segment-top-tip head-touch))))))
519 #+end_src
520 #+end_listing
523 #+caption: Program for detecting whether the worm has been wiggling for
524 #+caption: the last few frames. It uses a fourier analysis of the muscle
525 #+caption: contractions of the worm's tail to determine wiggling. This is
526 #+caption: signigicant because there is no particular frame that clearly
527 #+caption: indicates that the worm is wiggling --- only when multiple frames
528 #+caption: are analyzed together is the wiggling revealed. Defining
529 #+caption: wiggling this way also gives the worm an opportunity to learn
530 #+caption: and recognize ``frustrated wiggling'', where the worm tries to
531 #+caption: wiggle but can't. Frustrated wiggling is very visually different
532 #+caption: from actual wiggling, but this definition gives it to us for free.
533 #+name: wiggling
534 #+attr_latex: [htpb]
535 #+begin_listing clojure
536 #+begin_src clojure
537 (defn fft [nums]
538 (map
539 #(.getReal %)
540 (.transform
541 (FastFourierTransformer. DftNormalization/STANDARD)
542 (double-array nums) TransformType/FORWARD)))
544 (def indexed (partial map-indexed vector))
546 (defn max-indexed [s]
547 (first (sort-by (comp - second) (indexed s))))
549 (defn wiggling?
550 "Is the worm wiggling?"
551 [experiences]
552 (let [analysis-interval 0x40]
553 (when (> (count experiences) analysis-interval)
554 (let [a-flex 3
555 a-ex 2
556 muscle-activity
557 (map :muscle (vector:last-n experiences analysis-interval))
558 base-activity
559 (map #(- (% a-flex) (% a-ex)) muscle-activity)]
560 (= 2
561 (first
562 (max-indexed
563 (map #(Math/abs %)
564 (take 20 (fft base-activity))))))))))
565 #+end_src
566 #+end_listing
568 With these action predicates, I can now recognize the actions of
569 the worm while it is moving under my control and I have access to
570 all the worm's senses.
572 #+caption: Use the action predicates defined earlier to report on
573 #+caption: what the worm is doing while in simulation.
574 #+name: report-worm-activity
575 #+attr_latex: [htpb]
576 #+begin_listing clojure
577 #+begin_src clojure
578 (defn debug-experience
579 [experiences text]
580 (cond
581 (grand-circle? experiences) (.setText text "Grand Circle")
582 (curled? experiences) (.setText text "Curled")
583 (wiggling? experiences) (.setText text "Wiggling")
584 (resting? experiences) (.setText text "Resting")))
585 #+end_src
586 #+end_listing
588 #+caption: Using =debug-experience=, the body-centered predicates
589 #+caption: work together to classify the behaviour of the worm.
590 #+caption: the predicates are operating with access to the worm's
591 #+caption: full sensory data.
592 #+name: basic-worm-view
593 #+ATTR_LaTeX: :width 10cm
594 [[./images/worm-identify-init.png]]
596 These action predicates satisfy the recognition requirement of an
597 empathic recognition system. There is power in the simplicity of
598 the action predicates. They describe their actions without getting
599 confused in visual details of the worm. Each one is frame
600 independent, but more than that, they are each indepent of
601 irrelevant visual details of the worm and the environment. They
602 will work regardless of whether the worm is a different color or
603 hevaily textured, or if the environment has strange lighting.
605 The trick now is to make the action predicates work even when the
606 sensory data on which they depend is absent. If I can do that, then
607 I will have gained much,
609 ** \Phi-space describes the worm's experiences
611 As a first step towards building empathy, I need to gather all of
612 the worm's experiences during free play. I use a simple vector to
613 store all the experiences.
615 Each element of the experience vector exists in the vast space of
616 all possible worm-experiences. Most of this vast space is actually
617 unreachable due to physical constraints of the worm's body. For
618 example, the worm's segments are connected by hinge joints that put
619 a practical limit on the worm's range of motions without limiting
620 its degrees of freedom. Some groupings of senses are impossible;
621 the worm can not be bent into a circle so that its ends are
622 touching and at the same time not also experience the sensation of
623 touching itself.
625 As the worm moves around during free play and its experience vector
626 grows larger, the vector begins to define a subspace which is all
627 the sensations the worm can practicaly experience during normal
628 operation. I call this subspace \Phi-space, short for
629 physical-space. The experience vector defines a path through
630 \Phi-space. This path has interesting properties that all derive
631 from physical embodiment. The proprioceptive components are
632 completely smooth, because in order for the worm to move from one
633 position to another, it must pass through the intermediate
634 positions. The path invariably forms loops as actions are repeated.
635 Finally and most importantly, proprioception actually gives very
636 strong inference about the other senses. For example, when the worm
637 is flat, you can infer that it is touching the ground and that its
638 muscles are not active, because if the muscles were active, the
639 worm would be moving and would not be perfectly flat. In order to
640 stay flat, the worm has to be touching the ground, or it would
641 again be moving out of the flat position due to gravity. If the
642 worm is positioned in such a way that it interacts with itself,
643 then it is very likely to be feeling the same tactile feelings as
644 the last time it was in that position, because it has the same body
645 as then. If you observe multiple frames of proprioceptive data,
646 then you can become increasingly confident about the exact
647 activations of the worm's muscles, because it generally takes a
648 unique combination of muscle contractions to transform the worm's
649 body along a specific path through \Phi-space.
651 There is a simple way of taking \Phi-space and the total ordering
652 provided by an experience vector and reliably infering the rest of
653 the senses.
655 ** Empathy is the process of tracing though \Phi-space
657 Here is the core of a basic empathy algorithm, starting with an
658 experience vector:
660 First, group the experiences into tiered proprioceptive bins. I use
661 powers of 10 and 3 bins, and the smallest bin has an approximate
662 size of 0.001 radians in all proprioceptive dimensions.
664 Then, given a sequence of proprioceptive input, generate a set of
665 matching experience records for each input, using the tiered
666 proprioceptive bins.
668 Finally, to infer sensory data, select the longest consective chain
669 of experiences. Conecutive experience means that the experiences
670 appear next to each other in the experience vector.
672 This algorithm has three advantages:
674 1. It's simple
676 3. It's very fast -- retrieving possible interpretations takes
677 constant time. Tracing through chains of interpretations takes
678 time proportional to the average number of experiences in a
679 proprioceptive bin. Redundant experiences in \Phi-space can be
680 merged to save computation.
682 2. It protects from wrong interpretations of transient ambiguous
683 proprioceptive data. For example, if the worm is flat for just
684 an instant, this flattness will not be interpreted as implying
685 that the worm has its muscles relaxed, since the flattness is
686 part of a longer chain which includes a distinct pattern of
687 muscle activation. Markov chains or other memoryless statistical
688 models that operate on individual frames may very well make this
689 mistake.
691 #+caption: Program to convert an experience vector into a
692 #+caption: proprioceptively binned lookup function.
693 #+name: bin
694 #+attr_latex: [htpb]
695 #+begin_listing clojure
696 #+begin_src clojure
697 (defn bin [digits]
698 (fn [angles]
699 (->> angles
700 (flatten)
701 (map (juxt #(Math/sin %) #(Math/cos %)))
702 (flatten)
703 (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))
705 (defn gen-phi-scan
706 "Nearest-neighbors with binning. Only returns a result if
707 the propriceptive data is within 10% of a previously recorded
708 result in all dimensions."
709 [phi-space]
710 (let [bin-keys (map bin [3 2 1])
711 bin-maps
712 (map (fn [bin-key]
713 (group-by
714 (comp bin-key :proprioception phi-space)
715 (range (count phi-space)))) bin-keys)
716 lookups (map (fn [bin-key bin-map]
717 (fn [proprio] (bin-map (bin-key proprio))))
718 bin-keys bin-maps)]
719 (fn lookup [proprio-data]
720 (set (some #(% proprio-data) lookups)))))
721 #+end_src
722 #+end_listing
724 #+caption: =longest-thread= finds the longest path of consecutive
725 #+caption: experiences to explain proprioceptive worm data.
726 #+name: phi-space-history-scan
727 #+ATTR_LaTeX: :width 10cm
728 [[./images/aurellem-gray.png]]
730 =longest-thread= infers sensory data by stitching together pieces
731 from previous experience. It prefers longer chains of previous
732 experience to shorter ones. For example, during training the worm
733 might rest on the ground for one second before it performs its
734 excercises. If during recognition the worm rests on the ground for
735 five seconds, =longest-thread= will accomodate this five second
736 rest period by looping the one second rest chain five times.
738 =longest-thread= takes time proportinal to the average number of
739 entries in a proprioceptive bin, because for each element in the
740 starting bin it performes a series of set lookups in the preceeding
741 bins. If the total history is limited, then this is only a constant
742 multiple times the number of entries in the starting bin. This
743 analysis also applies even if the action requires multiple longest
744 chains -- it's still the average number of entries in a
745 proprioceptive bin times the desired chain length. Because
746 =longest-thread= is so efficient and simple, I can interpret
747 worm-actions in real time.
749 #+caption: Program to calculate empathy by tracing though \Phi-space
750 #+caption: and finding the longest (ie. most coherent) interpretation
751 #+caption: of the data.
752 #+name: longest-thread
753 #+attr_latex: [htpb]
754 #+begin_listing clojure
755 #+begin_src clojure
756 (defn longest-thread
757 "Find the longest thread from phi-index-sets. The index sets should
758 be ordered from most recent to least recent."
759 [phi-index-sets]
760 (loop [result '()
761 [thread-bases & remaining :as phi-index-sets] phi-index-sets]
762 (if (empty? phi-index-sets)
763 (vec result)
764 (let [threads
765 (for [thread-base thread-bases]
766 (loop [thread (list thread-base)
767 remaining remaining]
768 (let [next-index (dec (first thread))]
769 (cond (empty? remaining) thread
770 (contains? (first remaining) next-index)
771 (recur
772 (cons next-index thread) (rest remaining))
773 :else thread))))
774 longest-thread
775 (reduce (fn [thread-a thread-b]
776 (if (> (count thread-a) (count thread-b))
777 thread-a thread-b))
778 '(nil)
779 threads)]
780 (recur (concat longest-thread result)
781 (drop (count longest-thread) phi-index-sets))))))
782 #+end_src
783 #+end_listing
785 There is one final piece, which is to replace missing sensory data
786 with a best-guess estimate. While I could fill in missing data by
787 using a gradient over the closest known sensory data points,
788 averages can be misleading. It is certainly possible to create an
789 impossible sensory state by averaging two possible sensory states.
790 Therefore, I simply replicate the most recent sensory experience to
791 fill in the gaps.
793 #+caption: Fill in blanks in sensory experience by replicating the most
794 #+caption: recent experience.
795 #+name: infer-nils
796 #+attr_latex: [htpb]
797 #+begin_listing clojure
798 #+begin_src clojure
799 (defn infer-nils
800 "Replace nils with the next available non-nil element in the
801 sequence, or barring that, 0."
802 [s]
803 (loop [i (dec (count s))
804 v (transient s)]
805 (if (zero? i) (persistent! v)
806 (if-let [cur (v i)]
807 (if (get v (dec i) 0)
808 (recur (dec i) v)
809 (recur (dec i) (assoc! v (dec i) cur)))
810 (recur i (assoc! v i 0))))))
811 #+end_src
812 #+end_listing
814 ** Efficient action recognition with =EMPATH=
816 To use =EMPATH= with the worm, I first need to gather a set of
817 experiences from the worm that includes the actions I want to
818 recognize. The =generate-phi-space= program (listing
819 \ref{generate-phi-space} runs the worm through a series of
820 exercices and gatheres those experiences into a vector. The
821 =do-all-the-things= program is a routine expressed in a simple
822 muscle contraction script language for automated worm control. It
823 causes the worm to rest, curl, and wiggle over about 700 frames
824 (approx. 11 seconds).
826 #+caption: Program to gather the worm's experiences into a vector for
827 #+caption: further processing. The =motor-control-program= line uses
828 #+caption: a motor control script that causes the worm to execute a series
829 #+caption: of ``exercices'' that include all the action predicates.
830 #+name: generate-phi-space
831 #+attr_latex: [htpb]
832 #+begin_listing clojure
833 #+begin_src clojure
834 (def do-all-the-things
835 (concat
836 curl-script
837 [[300 :d-ex 40]
838 [320 :d-ex 0]]
839 (shift-script 280 (take 16 wiggle-script))))
841 (defn generate-phi-space []
842 (let [experiences (atom [])]
843 (run-world
844 (apply-map
845 worm-world
846 (merge
847 (worm-world-defaults)
848 {:end-frame 700
849 :motor-control
850 (motor-control-program worm-muscle-labels do-all-the-things)
851 :experiences experiences})))
852 @experiences))
853 #+end_src
854 #+end_listing
856 #+caption: Use longest thread and a phi-space generated from a short
857 #+caption: exercise routine to interpret actions during free play.
858 #+name: empathy-debug
859 #+attr_latex: [htpb]
860 #+begin_listing clojure
861 #+begin_src clojure
862 (defn init []
863 (def phi-space (generate-phi-space))
864 (def phi-scan (gen-phi-scan phi-space)))
866 (defn empathy-demonstration []
867 (let [proprio (atom ())]
868 (fn
869 [experiences text]
870 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
871 (swap! proprio (partial cons phi-indices))
872 (let [exp-thread (longest-thread (take 300 @proprio))
873 empathy (mapv phi-space (infer-nils exp-thread))]
874 (println-repl (vector:last-n exp-thread 22))
875 (cond
876 (grand-circle? empathy) (.setText text "Grand Circle")
877 (curled? empathy) (.setText text "Curled")
878 (wiggling? empathy) (.setText text "Wiggling")
879 (resting? empathy) (.setText text "Resting")
880 :else (.setText text "Unknown")))))))
882 (defn empathy-experiment [record]
883 (.start (worm-world :experience-watch (debug-experience-phi)
884 :record record :worm worm*)))
885 #+end_src
886 #+end_listing
888 The result of running =empathy-experiment= is that the system is
889 generally able to interpret worm actions using the action-predicates
890 on simulated sensory data just as well as with actual data. Figure
891 \ref{empathy-debug-image} was generated using =empathy-experiment=:
893 #+caption: From only proprioceptive data, =EMPATH= was able to infer
894 #+caption: the complete sensory experience and classify four poses
895 #+caption: (The last panel shows a composite image of \emph{wriggling},
896 #+caption: a dynamic pose.)
897 #+name: empathy-debug-image
898 #+ATTR_LaTeX: :width 10cm :placement [H]
899 [[./images/empathy-1.png]]
901 One way to measure the performance of =EMPATH= is to compare the
902 sutiability of the imagined sense experience to trigger the same
903 action predicates as the real sensory experience.
905 #+caption: Determine how closely empathy approximates actual
906 #+caption: sensory data.
907 #+name: test-empathy-accuracy
908 #+attr_latex: [htpb]
909 #+begin_listing clojure
910 #+begin_src clojure
911 (def worm-action-label
912 (juxt grand-circle? curled? wiggling?))
914 (defn compare-empathy-with-baseline [matches]
915 (let [proprio (atom ())]
916 (fn
917 [experiences text]
918 (let [phi-indices (phi-scan (:proprioception (peek experiences)))]
919 (swap! proprio (partial cons phi-indices))
920 (let [exp-thread (longest-thread (take 300 @proprio))
921 empathy (mapv phi-space (infer-nils exp-thread))
922 experience-matches-empathy
923 (= (worm-action-label experiences)
924 (worm-action-label empathy))]
925 (println-repl experience-matches-empathy)
926 (swap! matches #(conj % experience-matches-empathy)))))))
928 (defn accuracy [v]
929 (float (/ (count (filter true? v)) (count v))))
931 (defn test-empathy-accuracy []
932 (let [res (atom [])]
933 (run-world
934 (worm-world :experience-watch
935 (compare-empathy-with-baseline res)
936 :worm worm*))
937 (accuracy @res)))
938 #+end_src
939 #+end_listing
941 Running =test-empathy-accuracy= using the very short exercise
942 program defined in listing \ref{generate-phi-space}, and then doing
943 a similar pattern of activity manually yeilds an accuracy of around
944 73%. This is based on very limited worm experience. By training the
945 worm for longer, the accuracy dramatically improves.
947 #+caption: Program to generate \Phi-space using manual training.
948 #+name: manual-phi-space
949 #+attr_latex: [htpb]
950 #+begin_listing clojure
951 #+begin_src clojure
952 (defn init-interactive []
953 (def phi-space
954 (let [experiences (atom [])]
955 (run-world
956 (apply-map
957 worm-world
958 (merge
959 (worm-world-defaults)
960 {:experiences experiences})))
961 @experiences))
962 (def phi-scan (gen-phi-scan phi-space)))
963 #+end_src
964 #+end_listing
966 After about 1 minute of manual training, I was able to achieve 95%
967 accuracy on manual testing of the worm using =init-interactive= and
968 =test-empathy-accuracy=. The majority of errors are near the
969 boundaries of transitioning from one type of action to another.
970 During these transitions the exact label for the action is more open
971 to interpretation, and dissaggrement between empathy and experience
972 is more excusable.
974 ** Digression: bootstrapping touch using free exploration
976 In the previous section I showed how to compute actions in terms of
977 body-centered predicates which relied averate touch activation of
978 pre-defined regions of the worm's skin. What if, instead of recieving
979 touch pre-grouped into the six faces of each worm segment, the true
980 topology of the worm's skin was unknown? This is more similiar to how
981 a nerve fiber bundle might be arranged. While two fibers that are
982 close in a nerve bundle /might/ correspond to two touch sensors that
983 are close together on the skin, the process of taking a complicated
984 surface and forcing it into essentially a circle requires some cuts
985 and rerragenments.
987 In this section I show how to automatically learn the skin-topology of
988 a worm segment by free exploration. As the worm rolls around on the
989 floor, large sections of its surface get activated. If the worm has
990 stopped moving, then whatever region of skin that is touching the
991 floor is probably an important region, and should be recorded.
993 #+caption: Program to detect whether the worm is in a resting state
994 #+caption: with one face touching the floor.
995 #+name: pure-touch
996 #+begin_listing clojure
997 #+begin_src clojure
998 (def full-contact [(float 0.0) (float 0.1)])
1000 (defn pure-touch?
1001 "This is worm specific code to determine if a large region of touch
1002 sensors is either all on or all off."
1003 [[coords touch :as touch-data]]
1004 (= (set (map first touch)) (set full-contact)))
1005 #+end_src
1006 #+end_listing
1008 After collecting these important regions, there will many nearly
1009 similiar touch regions. While for some purposes the subtle
1010 differences between these regions will be important, for my
1011 purposes I colapse them into mostly non-overlapping sets using
1012 =remove-similiar= in listing \ref{remove-similiar}
1014 #+caption: Program to take a lits of set of points and ``collapse them''
1015 #+caption: so that the remaining sets in the list are siginificantly
1016 #+caption: different from each other. Prefer smaller sets to larger ones.
1017 #+name: remove-similiar
1018 #+begin_listing clojure
1019 #+begin_src clojure
1020 (defn remove-similar
1021 [coll]
1022 (loop [result () coll (sort-by (comp - count) coll)]
1023 (if (empty? coll) result
1024 (let [[x & xs] coll
1025 c (count x)]
1026 (if (some
1027 (fn [other-set]
1028 (let [oc (count other-set)]
1029 (< (- (count (union other-set x)) c) (* oc 0.1))))
1030 xs)
1031 (recur result xs)
1032 (recur (cons x result) xs))))))
1033 #+end_src
1034 #+end_listing
1036 Actually running this simulation is easy given =CORTEX='s facilities.
1038 #+caption: Collect experiences while the worm moves around. Filter the touch
1039 #+caption: sensations by stable ones, collapse similiar ones together,
1040 #+caption: and report the regions learned.
1041 #+name: learn-touch
1042 #+begin_listing clojure
1043 #+begin_src clojure
1044 (defn learn-touch-regions []
1045 (let [experiences (atom [])
1046 world (apply-map
1047 worm-world
1048 (assoc (worm-segment-defaults)
1049 :experiences experiences))]
1050 (run-world world)
1051 (->>
1052 @experiences
1053 (drop 175)
1054 ;; access the single segment's touch data
1055 (map (comp first :touch))
1056 ;; only deal with "pure" touch data to determine surfaces
1057 (filter pure-touch?)
1058 ;; associate coordinates with touch values
1059 (map (partial apply zipmap))
1060 ;; select those regions where contact is being made
1061 (map (partial group-by second))
1062 (map #(get % full-contact))
1063 (map (partial map first))
1064 ;; remove redundant/subset regions
1065 (map set)
1066 remove-similar)))
1068 (defn learn-and-view-touch-regions []
1069 (map view-touch-region
1070 (learn-touch-regions)))
1071 #+end_src
1072 #+end_listing
1074 The only thing remining to define is the particular motion the worm
1075 must take. I accomplish this with a simple motor control program.
1077 #+caption: Motor control program for making the worm roll on the ground.
1078 #+caption: This could also be replaced with random motion.
1079 #+name: worm-roll
1080 #+begin_listing clojure
1081 #+begin_src clojure
1082 (defn touch-kinesthetics []
1083 [[170 :lift-1 40]
1084 [190 :lift-1 19]
1085 [206 :lift-1 0]
1087 [400 :lift-2 40]
1088 [410 :lift-2 0]
1090 [570 :lift-2 40]
1091 [590 :lift-2 21]
1092 [606 :lift-2 0]
1094 [800 :lift-1 30]
1095 [809 :lift-1 0]
1097 [900 :roll-2 40]
1098 [905 :roll-2 20]
1099 [910 :roll-2 0]
1101 [1000 :roll-2 40]
1102 [1005 :roll-2 20]
1103 [1010 :roll-2 0]
1105 [1100 :roll-2 40]
1106 [1105 :roll-2 20]
1107 [1110 :roll-2 0]
1108 ])
1109 #+end_src
1110 #+end_listing
1113 #+caption: The small worm rolls around on the floor, driven
1114 #+caption: by the motor control program in listing \ref{worm-roll}.
1115 #+name: worm-roll
1116 #+ATTR_LaTeX: :width 12cm
1117 [[./images/worm-roll.png]]
1120 #+caption: After completing its adventures, the worm now knows
1121 #+caption: how its touch sensors are arranged along its skin. These
1122 #+caption: are the regions that were deemed important by
1123 #+caption: =learn-touch-regions=. Note that the worm has discovered
1124 #+caption: that it has six sides.
1125 #+name: worm-touch-map
1126 #+ATTR_LaTeX: :width 12cm
1127 [[./images/touch-learn.png]]
1129 While simple, =learn-touch-regions= exploits regularities in both
1130 the worm's physiology and the worm's environment to correctly
1131 deduce that the worm has six sides. Note that =learn-touch-regions=
1132 would work just as well even if the worm's touch sense data were
1133 completely scrambled. The cross shape is just for convienence. This
1134 example justifies the use of pre-defined touch regions in =EMPATH=.
1136 * Contributions
1141 # An anatomical joke:
1142 # - Training
1143 # - Skeletal imitation
1144 # - Sensory fleshing-out
1145 # - Classification