Mercurial > cortex

annotate thesis/dylan-cortex-diff.diff @ 560:1e5b5eb8c576

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
solved problem with biblography.
author Robert McIntyre <rlm@mit.edu>
date Fri, 02 May 2014 15:36:36 -0400
parents 90b236381642
children
rev   line source
rlm@513 1 diff -r f639e2139ce2 thesis/cortex.org
rlm@513 2 --- a/thesis/cortex.org Sun Mar 30 01:34:43 2014 -0400
rlm@513 3 +++ b/thesis/cortex.org Sun Mar 30 10:07:17 2014 -0400
rlm@513 4 @@ -41,49 +41,46 @@
rlm@513 5 [[./images/aurellem-gray.png]]
rlm@513 6
rlm@513 7
rlm@513 8 -* Empathy and Embodiment as problem solving strategies
rlm@513 9 +* Empathy \& Embodiment: problem solving strategies
rlm@513 10
rlm@513 11 - By the end of this thesis, you will have seen a novel approach to
rlm@513 12 - interpreting video using embodiment and empathy. You will have also
rlm@513 13 - seen one way to efficiently implement empathy for embodied
rlm@513 14 - creatures. Finally, you will become familiar with =CORTEX=, a system
rlm@513 15 - for designing and simulating creatures with rich senses, which you
rlm@513 16 - may choose to use in your own research.
rlm@513 17 -
rlm@513 18 - This is the core vision of my thesis: That one of the important ways
rlm@513 19 - in which we understand others is by imagining ourselves in their
rlm@513 20 - position and emphatically feeling experiences relative to our own
rlm@513 21 - bodies. By understanding events in terms of our own previous
rlm@513 22 - corporeal experience, we greatly constrain the possibilities of what
rlm@513 23 - would otherwise be an unwieldy exponential search. This extra
rlm@513 24 - constraint can be the difference between easily understanding what
rlm@513 25 - is happening in a video and being completely lost in a sea of
rlm@513 26 - incomprehensible color and movement.
rlm@513 27 -
rlm@513 28 -** Recognizing actions in video is extremely difficult
rlm@513 29 -
rlm@513 30 - Consider for example the problem of determining what is happening
rlm@513 31 - in a video of which this is one frame:
rlm@513 32 -
rlm@513 33 +** The problem: recognizing actions in video is extremely difficult
rlm@513 34 +# developing / requires useful representations
rlm@513 35 +
rlm@513 36 + Examine the following collection of images. As you, and indeed very
rlm@513 37 + young children, can easily determine, each one is a picture of
rlm@513 38 + someone drinking.
rlm@513 39 +
rlm@513 40 + # dxh: cat, cup, drinking fountain, rain, straw, coconut
rlm@513 41 #+caption: A cat drinking some water. Identifying this action is
rlm@513 42 - #+caption: beyond the state of the art for computers.
rlm@513 43 + #+caption: beyond the capabilities of existing computer vision systems.
rlm@513 44 #+ATTR_LaTeX: :width 7cm
rlm@513 45 [[./images/cat-drinking.jpg]]
rlm@513 46 +
rlm@513 47 + Nevertheless, it is beyond the state of the art for a computer
rlm@513 48 + vision program to describe what's happening in each of these
rlm@513 49 + images, or what's common to them. Part of the problem is that many
rlm@513 50 + computer vision systems focus on pixel-level details or probability
rlm@513 51 + distributions of pixels, with little focus on [...]
rlm@513 52 +
rlm@513 53 +
rlm@513 54 + In fact, the contents of scene may have much less to do with pixel
rlm@513 55 + probabilities than with recognizing various affordances: things you
rlm@513 56 + can move, objects you can grasp, spaces that can be filled
rlm@513 57 + (Gibson). For example, what processes might enable you to see the
rlm@513 58 + chair in figure \ref{hidden-chair}?
rlm@513 59 + # Or suppose that you are building a program that recognizes chairs.
rlm@513 60 + # How could you ``see'' the chair ?
rlm@513 61
rlm@513 62 - It is currently impossible for any computer program to reliably
rlm@513 63 - label such a video as ``drinking''. And rightly so -- it is a very
rlm@513 64 - hard problem! What features can you describe in terms of low level
rlm@513 65 - functions of pixels that can even begin to describe at a high level
rlm@513 66 - what is happening here?
rlm@513 67 -
rlm@513 68 - Or suppose that you are building a program that recognizes chairs.
rlm@513 69 - How could you ``see'' the chair in figure \ref{hidden-chair}?
rlm@513 70 -
rlm@513 71 + # dxh: blur chair
rlm@513 72 #+caption: The chair in this image is quite obvious to humans, but I
rlm@513 73 #+caption: doubt that any modern computer vision program can find it.
rlm@513 74 #+name: hidden-chair
rlm@513 75 #+ATTR_LaTeX: :width 10cm
rlm@513 76 [[./images/fat-person-sitting-at-desk.jpg]]
rlm@513 77 +
rlm@513 78 +
rlm@513 79 +
rlm@513 80 +
rlm@513 81
rlm@513 82 Finally, how is it that you can easily tell the difference between
rlm@513 83 how the girls /muscles/ are working in figure \ref{girl}?
rlm@513 84 @@ -95,10 +92,13 @@
rlm@513 85 #+ATTR_LaTeX: :width 7cm
rlm@513 86 [[./images/wall-push.png]]
rlm@513 87
rlm@513 88 +
rlm@513 89 +
rlm@513 90 +
rlm@513 91 Each of these examples tells us something about what might be going
rlm@513 92 on in our minds as we easily solve these recognition problems.
rlm@513 93
rlm@513 94 - The hidden chairs show us that we are strongly triggered by cues
rlm@513 95 + The hidden chair shows us that we are strongly triggered by cues
rlm@513 96 relating to the position of human bodies, and that we can determine
rlm@513 97 the overall physical configuration of a human body even if much of
rlm@513 98 that body is occluded.
rlm@513 99 @@ -109,10 +109,107 @@
rlm@513 100 most positions, and we can easily project this self-knowledge to
rlm@513 101 imagined positions triggered by images of the human body.
rlm@513 102
rlm@513 103 -** =EMPATH= neatly solves recognition problems
rlm@513 104 +** A step forward: the sensorimotor-centered approach
rlm@513 105 +# ** =EMPATH= recognizes what creatures are doing
rlm@513 106 +# neatly solves recognition problems
rlm@513 107 + In this thesis, I explore the idea that our knowledge of our own
rlm@513 108 + bodies enables us to recognize the actions of others.
rlm@513 109 +
rlm@513 110 + First, I built a system for constructing virtual creatures with
rlm@513 111 + physiologically plausible sensorimotor systems and detailed
rlm@513 112 + environments. The result is =CORTEX=, which is described in section
rlm@513 113 + \ref{sec-2}. (=CORTEX= was built to be flexible and useful to other
rlm@513 114 + AI researchers; it is provided in full with detailed instructions
rlm@513 115 + on the web [here].)
rlm@513 116 +
rlm@513 117 + Next, I wrote routines which enabled a simple worm-like creature to
rlm@513 118 + infer the actions of a second worm-like creature, using only its
rlm@513 119 + own prior sensorimotor experiences and knowledge of the second
rlm@513 120 + worm's joint positions. This program, =EMPATH=, is described in
rlm@513 121 + section \ref{sec-3}, and the key results of this experiment are
rlm@513 122 + summarized below.
rlm@513 123 +
rlm@513 124 + #+caption: From only \emph{proprioceptive} data, =EMPATH= was able to infer
rlm@513 125 + #+caption: the complete sensory experience and classify these four poses.
rlm@513 126 + #+caption: The last image is a composite, depicting the intermediate stages of \emph{wriggling}.
rlm@513 127 + #+name: worm-recognition-intro-2
rlm@513 128 + #+ATTR_LaTeX: :width 15cm
rlm@513 129 + [[./images/empathy-1.png]]
rlm@513 130 +
rlm@513 131 + # =CORTEX= provides a language for describing the sensorimotor
rlm@513 132 + # experiences of various creatures.
rlm@513 133 +
rlm@513 134 + # Next, I developed an experiment to test the power of =CORTEX='s
rlm@513 135 + # sensorimotor-centered language for solving recognition problems. As
rlm@513 136 + # a proof of concept, I wrote routines which enabled a simple
rlm@513 137 + # worm-like creature to infer the actions of a second worm-like
rlm@513 138 + # creature, using only its own previous sensorimotor experiences and
rlm@513 139 + # knowledge of the second worm's joints (figure
rlm@513 140 + # \ref{worm-recognition-intro-2}). The result of this proof of
rlm@513 141 + # concept was the program =EMPATH=, described in section
rlm@513 142 + # \ref{sec-3}. The key results of this
rlm@513 143 +
rlm@513 144 + # Using only first-person sensorimotor experiences and third-person
rlm@513 145 + # proprioceptive data,
rlm@513 146 +
rlm@513 147 +*** Key results
rlm@513 148 + - After one-shot supervised training, =EMPATH= was able recognize a
rlm@513 149 + wide variety of static poses and dynamic actions---ranging from
rlm@513 150 + curling in a circle to wriggling with a particular frequency ---
rlm@513 151 + with 95\% accuracy.
rlm@513 152 + - These results were completely independent of viewing angle
rlm@513 153 + because the underlying body-centered language fundamentally is;
rlm@513 154 + once an action is learned, it can be recognized equally well from
rlm@513 155 + any viewing angle.
rlm@513 156 + - =EMPATH= is surprisingly short; the sensorimotor-centered
rlm@513 157 + language provided by =CORTEX= resulted in extremely economical
rlm@513 158 + recognition routines --- about 0000 lines in all --- suggesting
rlm@513 159 + that such representations are very powerful, and often
rlm@513 160 + indispensible for the types of recognition tasks considered here.
rlm@513 161 + - Although for expediency's sake, I relied on direct knowledge of
rlm@513 162 + joint positions in this proof of concept, it would be
rlm@513 163 + straightforward to extend =EMPATH= so that it (more
rlm@513 164 + realistically) infers joint positions from its visual data.
rlm@513 165 +
rlm@513 166 +# because the underlying language is fundamentally orientation-independent
rlm@513 167 +
rlm@513 168 +# recognize the actions of a worm with 95\% accuracy. The
rlm@513 169 +# recognition tasks
rlm@513 170
rlm@513 171 - I propose a system that can express the types of recognition
rlm@513 172 - problems above in a form amenable to computation. It is split into
rlm@513 173 +
rlm@513 174 +
rlm@513 175 +
rlm@513 176 + [Talk about these results and what you find promising about them]
rlm@513 177 +
rlm@513 178 +** Roadmap
rlm@513 179 + [I'm going to explain how =CORTEX= works, then break down how
rlm@513 180 + =EMPATH= does its thing. Because the details reveal such-and-such
rlm@513 181 + about the approach.]
rlm@513 182 +
rlm@513 183 + # The success of this simple proof-of-concept offers a tantalizing
rlm@513 184 +
rlm@513 185 +
rlm@513 186 + # explore the idea
rlm@513 187 + # The key contribution of this thesis is the idea that body-centered
rlm@513 188 + # representations (which express
rlm@513 189 +
rlm@513 190 +
rlm@513 191 + # the
rlm@513 192 + # body-centered approach --- in which I try to determine what's
rlm@513 193 + # happening in a scene by bringing it into registration with my own
rlm@513 194 + # bodily experiences --- are indispensible for recognizing what
rlm@513 195 + # creatures are doing in a scene.
rlm@513 196 +
rlm@513 197 +* COMMENT
rlm@513 198 +# body-centered language
rlm@513 199 +
rlm@513 200 + In this thesis, I'll describe =EMPATH=, which solves a certain
rlm@513 201 + class of recognition problems
rlm@513 202 +
rlm@513 203 + The key idea is to use self-centered (or first-person) language.
rlm@513 204 +
rlm@513 205 + I have built a system that can express the types of recognition
rlm@513 206 + problems in a form amenable to computation. It is split into
rlm@513 207 four parts:
rlm@513 208
rlm@513 209 - Free/Guided Play :: The creature moves around and experiences the
rlm@513 210 @@ -286,14 +383,14 @@
rlm@513 211 code to create a creature, and can use a wide library of
rlm@513 212 pre-existing blender models as a base for your own creatures.
rlm@513 213
rlm@513 214 - - =CORTEX= implements a wide variety of senses, including touch,
rlm@513 215 + - =CORTEX= implements a wide variety of senses: touch,
rlm@513 216 proprioception, vision, hearing, and muscle tension. Complicated
rlm@513 217 senses like touch, and vision involve multiple sensory elements
rlm@513 218 embedded in a 2D surface. You have complete control over the
rlm@513 219 distribution of these sensor elements through the use of simple
rlm@513 220 png image files. In particular, =CORTEX= implements more
rlm@513 221 comprehensive hearing than any other creature simulation system
rlm@513 222 - available.
rlm@513 223 + available.
rlm@513 224
rlm@513 225 - =CORTEX= supports any number of creatures and any number of
rlm@513 226 senses. Time in =CORTEX= dialates so that the simulated creatures
rlm@513 227 @@ -353,7 +450,24 @@
rlm@513 228 \end{sidewaysfigure}
rlm@513 229 #+END_LaTeX
rlm@513 230
rlm@513 231 -** Contributions
rlm@513 232 +** Road map
rlm@513 233 +
rlm@513 234 + By the end of this thesis, you will have seen a novel approach to
rlm@513 235 + interpreting video using embodiment and empathy. You will have also
rlm@513 236 + seen one way to efficiently implement empathy for embodied
rlm@513 237 + creatures. Finally, you will become familiar with =CORTEX=, a system
rlm@513 238 + for designing and simulating creatures with rich senses, which you
rlm@513 239 + may choose to use in your own research.
rlm@513 240 +
rlm@513 241 + This is the core vision of my thesis: That one of the important ways
rlm@513 242 + in which we understand others is by imagining ourselves in their
rlm@513 243 + position and emphatically feeling experiences relative to our own
rlm@513 244 + bodies. By understanding events in terms of our own previous
rlm@513 245 + corporeal experience, we greatly constrain the possibilities of what
rlm@513 246 + would otherwise be an unwieldy exponential search. This extra
rlm@513 247 + constraint can be the difference between easily understanding what
rlm@513 248 + is happening in a video and being completely lost in a sea of
rlm@513 249 + incomprehensible color and movement.
rlm@513 250
rlm@513 251 - I built =CORTEX=, a comprehensive platform for embodied AI
rlm@513 252 experiments. =CORTEX= supports many features lacking in other
rlm@513 253 @@ -363,18 +477,22 @@
rlm@513 254 - I built =EMPATH=, which uses =CORTEX= to identify the actions of
rlm@513 255 a worm-like creature using a computational model of empathy.
rlm@513 256
rlm@513 257 -* Building =CORTEX=
rlm@513 258 -
rlm@513 259 - I intend for =CORTEX= to be used as a general-purpose library for
rlm@513 260 - building creatures and outfitting them with senses, so that it will
rlm@513 261 - be useful for other researchers who want to test out ideas of their
rlm@513 262 - own. To this end, wherver I have had to make archetictural choices
rlm@513 263 - about =CORTEX=, I have chosen to give as much freedom to the user as
rlm@513 264 - possible, so that =CORTEX= may be used for things I have not
rlm@513 265 - forseen.
rlm@513 266 -
rlm@513 267 -** Simulation or Reality?
rlm@513 268 -
rlm@513 269 +
rlm@513 270 +* Designing =CORTEX=
rlm@513 271 + In this section, I outline the design decisions that went into
rlm@513 272 + making =CORTEX=, along with some details about its
rlm@513 273 + implementation. (A practical guide to getting started with =CORTEX=,
rlm@513 274 + which skips over the history and implementation details presented
rlm@513 275 + here, is provided in an appendix \ref{} at the end of this paper.)
rlm@513 276 +
rlm@513 277 + Throughout this project, I intended for =CORTEX= to be flexible and
rlm@513 278 + extensible enough to be useful for other researchers who want to
rlm@513 279 + test out ideas of their own. To this end, wherver I have had to make
rlm@513 280 + archetictural choices about =CORTEX=, I have chosen to give as much
rlm@513 281 + freedom to the user as possible, so that =CORTEX= may be used for
rlm@513 282 + things I have not forseen.
rlm@513 283 +
rlm@513 284 +** Building in simulation versus reality
rlm@513 285 The most important archetictural decision of all is the choice to
rlm@513 286 use a computer-simulated environemnt in the first place! The world
rlm@513 287 is a vast and rich place, and for now simulations are a very poor