rlm@425: #+title: =CORTEX=
rlm@425: #+author: Robert McIntyre
rlm@425: #+email: rlm@mit.edu
rlm@425: #+description: Using embodied AI to facilitate Artificial Imagination.
rlm@425: #+keywords: AI, clojure, embodiment
rlm@422: 
rlm@437: 
rlm@439: * Empathy and Embodiment as problem solving strategies
rlm@437:   
rlm@437:   By the end of this thesis, you will have seen a novel approach to
rlm@437:   interpreting video using embodiment and empathy. You will have also
rlm@437:   seen one way to efficiently implement empathy for embodied
rlm@441:   creatures. Finally, you will become familiar with =CORTEX=, a
rlm@441:   system for designing and simulating creatures with rich senses,
rlm@441:   which you may choose to use in your own research.
rlm@437:   
rlm@441:   This is the core vision of my thesis: That one of the important ways
rlm@441:   in which we understand others is by imagining ourselves in their
rlm@441:   position and emphatically feeling experiences relative to our own
rlm@441:   bodies. By understanding events in terms of our own previous
rlm@441:   corporeal experience, we greatly constrain the possibilities of what
rlm@441:   would otherwise be an unwieldy exponential search. This extra
rlm@441:   constraint can be the difference between easily understanding what
rlm@441:   is happening in a video and being completely lost in a sea of
rlm@441:   incomprehensible color and movement.
rlm@435: 
rlm@436: ** Recognizing actions in video is extremely difficult
rlm@437: 
rlm@441:    Consider for example the problem of determining what is happening in
rlm@441:    a video of which this is one frame:
rlm@437: 
rlm@441:    #+caption: A cat drinking some water. Identifying this action is 
rlm@441:    #+caption: beyond the state of the art for computers.
rlm@441:    #+ATTR_LaTeX: :width 7cm
rlm@441:    [[./images/cat-drinking.jpg]]
rlm@441:    
rlm@441:    It is currently impossible for any computer program to reliably
rlm@442:    label such a video as "drinking".  And rightly so -- it is a very
rlm@441:    hard problem! What features can you describe in terms of low level
rlm@441:    functions of pixels that can even begin to describe at a high level
rlm@441:    what is happening here?
rlm@437:   
rlm@441:    Or suppose that you are building a program that recognizes
rlm@441:    chairs. How could you ``see'' the chair in figure
rlm@441:    \ref{invisible-chair} and figure \ref{hidden-chair}?
rlm@441:    
rlm@441:    #+caption: When you look at this, do you think ``chair''? I certainly do.
rlm@441:    #+name: invisible-chair
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@441:    [[./images/invisible-chair.png]]
rlm@441:    
rlm@441:    #+caption: The chair in this image is quite obvious to humans, but I 
rlm@441:    #+caption: doubt that any computer program can find it.
rlm@441:    #+name: hidden-chair
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@441:    [[./images/fat-person-sitting-at-desk.jpg]]
rlm@441:    
rlm@441:    Finally, how is it that you can easily tell the difference between
rlm@441:    how the girls /muscles/ are working in figure \ref{girl}?
rlm@441:    
rlm@441:    #+caption: The mysterious ``common sense'' appears here as you are able 
rlm@441:    #+caption: to discern the difference in how the girl's arm muscles
rlm@441:    #+caption: are activated between the two images.
rlm@441:    #+name: girl
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@441:    [[./images/wall-push.png]]
rlm@437:   
rlm@441:    Each of these examples tells us something about what might be going
rlm@441:    on in our minds as we easily solve these recognition problems.
rlm@441:    
rlm@441:    The hidden chairs show us that we are strongly triggered by cues
rlm@441:    relating to the position of human bodies, and that we can
rlm@441:    determine the overall physical configuration of a human body even
rlm@441:    if much of that body is occluded.
rlm@437: 
rlm@441:    The picture of the girl pushing against the wall tells us that we
rlm@441:    have common sense knowledge about the kinetics of our own bodies.
rlm@441:    We know well how our muscles would have to work to maintain us in
rlm@441:    most positions, and we can easily project this self-knowledge to
rlm@441:    imagined positions triggered by images of the human body.
rlm@441: 
rlm@441: ** =EMPATH= neatly solves recognition problems  
rlm@441:    
rlm@441:    I propose a system that can express the types of recognition
rlm@441:    problems above in a form amenable to computation. It is split into
rlm@441:    four parts:
rlm@441: 
rlm@441:    - Free/Guided Play :: The creature moves around and experiences the
rlm@441:         world through its unique perspective. Many otherwise
rlm@441:         complicated actions are easily described in the language of a
rlm@441:         full suite of body-centered, rich senses. For example,
rlm@441:         drinking is the feeling of water sliding down your throat, and
rlm@441:         cooling your insides. It's often accompanied by bringing your
rlm@441:         hand close to your face, or bringing your face close to
rlm@441:         water. Sitting down is the feeling of bending your knees,
rlm@441:         activating your quadriceps, then feeling a surface with your
rlm@441:         bottom and relaxing your legs. These body-centered action
rlm@441:         descriptions can be either learned or hard coded.
rlm@441:    - Alignment :: When trying to interpret a video or image, the
rlm@441:                   creature takes a model of itself and aligns it with
rlm@441:                   whatever it sees. This can be a rather loose
rlm@441:                   alignment that can cross species, as when humans try
rlm@441:                   to align themselves with things like ponies, dogs,
rlm@441:                   or other humans with a different body type.
rlm@441:    - Empathy :: The alignment triggers the memories of previous
rlm@441:                 experience. For example, the alignment itself easily
rlm@441:                 maps to proprioceptive data. Any sounds or obvious
rlm@441:                 skin contact in the video can to a lesser extent
rlm@441:                 trigger previous experience. The creatures previous
rlm@441:                 experience is chained together in short bursts to
rlm@441:                 coherently describe the new scene.
rlm@441:    - Recognition :: With the scene now described in terms of past
rlm@441:                     experience, the creature can now run its
rlm@441:                     action-identification programs on this synthesized
rlm@441:                     sensory data, just as it would if it were actually
rlm@441:                     experiencing the scene first-hand. If previous
rlm@441:                     experience has been accurately retrieved, and if
rlm@441:                     it is analogous enough to the scene, then the
rlm@441:                     creature will correctly identify the action in the
rlm@441:                     scene.
rlm@441: 		    
rlm@441: 
rlm@441:    For example, I think humans are able to label the cat video as
rlm@441:    "drinking" because they imagine /themselves/ as the cat, and
rlm@441:    imagine putting their face up against a stream of water and
rlm@441:    sticking out their tongue. In that imagined world, they can feel
rlm@441:    the cool water hitting their tongue, and feel the water entering
rlm@441:    their body, and are able to recognize that /feeling/ as
rlm@441:    drinking. So, the label of the action is not really in the pixels
rlm@441:    of the image, but is found clearly in a simulation inspired by
rlm@441:    those pixels. An imaginative system, having been trained on
rlm@441:    drinking and non-drinking examples and learning that the most
rlm@441:    important component of drinking is the feeling of water sliding
rlm@441:    down one's throat, would analyze a video of a cat drinking in the
rlm@441:    following manner:
rlm@441:    
rlm@441:    1. Create a physical model of the video by putting a "fuzzy" model
rlm@441:       of its own body in place of the cat. Possibly also create a
rlm@441:       simulation of the stream of water.
rlm@441: 
rlm@441:    2. Play out this simulated scene and generate imagined sensory
rlm@441:       experience. This will include relevant muscle contractions, a
rlm@441:       close up view of the stream from the cat's perspective, and most
rlm@441:       importantly, the imagined feeling of water entering the
rlm@443:       mouth. The imagined sensory experience can come from a
rlm@441:       simulation of the event, but can also be pattern-matched from
rlm@441:       previous, similar embodied experience.
rlm@441: 
rlm@441:    3. The action is now easily identified as drinking by the sense of
rlm@441:       taste alone. The other senses (such as the tongue moving in and
rlm@441:       out) help to give plausibility to the simulated action. Note that
rlm@441:       the sense of vision, while critical in creating the simulation,
rlm@441:       is not critical for identifying the action from the simulation.
rlm@441: 
rlm@441:    For the chair examples, the process is even easier:
rlm@441: 
rlm@441:     1. Align a model of your body to the person in the image.
rlm@441: 
rlm@441:     2. Generate proprioceptive sensory data from this alignment.
rlm@437:   
rlm@441:     3. Use the imagined proprioceptive data as a key to lookup related
rlm@441:        sensory experience associated with that particular proproceptive
rlm@441:        feeling.
rlm@437: 
rlm@443:     4. Retrieve the feeling of your bottom resting on a surface, your
rlm@443:        knees bent, and your leg muscles relaxed.
rlm@437: 
rlm@441:     5. This sensory information is consistent with the =sitting?=
rlm@441:        sensory predicate, so you (and the entity in the image) must be
rlm@441:        sitting.
rlm@440: 
rlm@441:     6. There must be a chair-like object since you are sitting.
rlm@440: 
rlm@441:    Empathy offers yet another alternative to the age-old AI
rlm@441:    representation question: ``What is a chair?'' --- A chair is the
rlm@441:    feeling of sitting.
rlm@441: 
rlm@441:    My program, =EMPATH= uses this empathic problem solving technique
rlm@441:    to interpret the actions of a simple, worm-like creature. 
rlm@437:    
rlm@441:    #+caption: The worm performs many actions during free play such as 
rlm@441:    #+caption: curling, wiggling, and resting.
rlm@441:    #+name: worm-intro
rlm@443:    #+ATTR_LaTeX: :width 13cm
rlm@445:    [[./images/worm-intro-white.png]]
rlm@437: 
rlm@437: 
rlm@441:    #+caption: Body-centerd actions are best expressed in a body-centered 
rlm@441:    #+caption: language. This code detects when the worm has curled into a 
rlm@441:    #+caption: full circle. Imagine how you would replicate this functionality
rlm@441:    #+caption: using low-level pixel features such as HOG filters!
rlm@441:    #+name: grand-circle-intro
rlm@441:    #+begin_listing clojure
rlm@441:    #+begin_src clojure
rlm@441: (defn grand-circle?
rlm@441:   "Does the worm form a majestic circle (one end touching the other)?"
rlm@441:   [experiences]
rlm@441:   (and (curled? experiences)
rlm@441:        (let [worm-touch (:touch (peek experiences))
rlm@441:              tail-touch (worm-touch 0)
rlm@441:              head-touch (worm-touch 4)]
rlm@441:          (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))
rlm@441:               (< 0.55 (contact worm-segment-top-tip    head-touch))))))
rlm@441:    #+end_src
rlm@441:    #+end_listing
rlm@437: 
rlm@441:    #+caption: The actions of a worm in a video can be recognized by
rlm@441:    #+caption: proprioceptive data and sentory predicates by filling
rlm@441:    #+caption:  in the missing sensory detail with previous experience.
rlm@441:    #+name: worm-recognition-intro
rlm@441:    #+ATTR_LaTeX: :width 10cm
rlm@445:    [[./images/worm-poses.png]]
rlm@437: 
rlm@441:    
rlm@441:    One powerful advantage of empathic problem solving is that it
rlm@441:    factors the action recognition problem into two easier problems. To
rlm@441:    use empathy, you need an /aligner/, which takes the video and a
rlm@441:    model of your body, and aligns the model with the video. Then, you
rlm@441:    need a /recognizer/, which uses the aligned model to interpret the
rlm@441:    action. The power in this method lies in the fact that you describe
rlm@441:    all actions form a body-centered, rich viewpoint. This way, if you
rlm@441:    teach the system what ``running'' is, and you have a good enough
rlm@441:    aligner, the system will from then on be able to recognize running
rlm@441:    from any point of view, even strange points of view like above or
rlm@441:    underneath the runner. This is in contrast to action recognition
rlm@441:    schemes that try to identify actions using a non-embodied approach
rlm@441:    such as TODO:REFERENCE. If these systems learn about running as viewed
rlm@441:    from the side, they will not automatically be able to recognize
rlm@441:    running from any other viewpoint.
rlm@441: 
rlm@441:    Another powerful advantage is that using the language of multiple
rlm@441:    body-centered rich senses to describe body-centerd actions offers a
rlm@441:    massive boost in descriptive capability. Consider how difficult it
rlm@441:    would be to compose a set of HOG filters to describe the action of
rlm@441:    a simple worm-creature "curling" so that its head touches its tail,
rlm@441:    and then behold the simplicity of describing thus action in a
rlm@441:    language designed for the task (listing \ref{grand-circle-intro}):
rlm@441: 
rlm@435: 
rlm@437: ** =CORTEX= is a toolkit for building sensate creatures
rlm@435: 
rlm@436:    Hand integration demo
rlm@435: 
rlm@437: ** Contributions
rlm@435: 
rlm@436: * Building =CORTEX=
rlm@435: 
rlm@436: ** To explore embodiment, we need a world, body, and senses
rlm@435: 
rlm@436: ** Because of Time, simulation is perferable to reality
rlm@435: 
rlm@436: ** Video game engines are a great starting point
rlm@435: 
rlm@436: ** Bodies are composed of segments connected by joints
rlm@435: 
rlm@436: ** Eyes reuse standard video game components
rlm@436: 
rlm@436: ** Hearing is hard; =CORTEX= does it right
rlm@436: 
rlm@436: ** Touch uses hundreds of hair-like elements
rlm@436: 
rlm@440: ** Proprioception is the sense that makes everything ``real''
rlm@436: 
rlm@436: ** Muscles are both effectors and sensors
rlm@436: 
rlm@436: ** =CORTEX= brings complex creatures to life!
rlm@436: 
rlm@436: ** =CORTEX= enables many possiblities for further research
rlm@435: 
rlm@435: * Empathy in a simulated worm
rlm@435: 
rlm@436: ** Embodiment factors action recognition into managable parts
rlm@435: 
rlm@436: ** Action recognition is easy with a full gamut of senses
rlm@435: 
rlm@437: ** Digression: bootstrapping touch using free exploration
rlm@435: 
rlm@436: ** \Phi-space describes the worm's experiences
rlm@435: 
rlm@436: ** Empathy is the process of tracing though \Phi-space 
rlm@435:   
rlm@441: ** Efficient action recognition with =EMPATH=
rlm@425: 
rlm@432: * Contributions
rlm@432:   - Built =CORTEX=, a comprehensive platform for embodied AI
rlm@432:     experiments. Has many new features lacking in other systems, such
rlm@432:     as sound. Easy to model/create new creatures.
rlm@432:   - created a novel concept for action recognition by using artificial
rlm@432:     imagination. 
rlm@426: 
rlm@436: In the second half of the thesis I develop a computational model of
rlm@436: empathy, using =CORTEX= as a base. Empathy in this context is the
rlm@436: ability to observe another creature and infer what sorts of sensations
rlm@436: that creature is feeling. My empathy algorithm involves multiple
rlm@436: phases. First is free-play, where the creature moves around and gains
rlm@436: sensory experience. From this experience I construct a representation
rlm@436: of the creature's sensory state space, which I call \phi-space. Using
rlm@436: \phi-space, I construct an efficient function for enriching the
rlm@436: limited data that comes from observing another creature with a full
rlm@436: compliment of imagined sensory data based on previous experience. I
rlm@436: can then use the imagined sensory data to recognize what the observed
rlm@436: creature is doing and feeling, using straightforward embodied action
rlm@436: predicates. This is all demonstrated with using a simple worm-like
rlm@436: creature, and recognizing worm-actions based on limited data.
rlm@432: 
rlm@436: Embodied representation using multiple senses such as touch,
rlm@436: proprioception, and muscle tension turns out be be exceedingly
rlm@436: efficient at describing body-centered actions. It is the ``right
rlm@436: language for the job''. For example, it takes only around 5 lines of
rlm@436: LISP code to describe the action of ``curling'' using embodied
rlm@436: primitives. It takes about 8 lines to describe the seemingly
rlm@436: complicated action of wiggling.
rlm@432: 
rlm@437: 
rlm@437: 
rlm@437: * COMMENT names for cortex
rlm@437:  - bioland