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author Robert McIntyre <rlm@mit.edu>
date Sun, 19 Apr 2015 04:01:53 -0700
parents 202c6d19acad
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rlm@572 1 Here I demonstrate the power of using embodied artificial intelligence
rlm@572 2 to attack the \emph{action recognition} problem, which is the challenge of
rlm@572 3 recognizing actions performed by a creature given limited data about
rlm@572 4 the creature's actions, such as a video recording. I solve this
rlm@572 5 problem in the case of a worm-like creature performing actions such as
rlm@572 6 curling and wiggling.
rlm@572 7
rlm@572 8 To attack the action recognition problem, I developed a computational
rlm@572 9 model of empathy (\texttt{EMPATH}) which allows me to recognize actions using
rlm@572 10 simple, embodied representations of actions (which require rich
rlm@572 11 sensory data), even when that sensory data is not actually available.
rlm@572 12 The missing sense data is imagined by combining previous experiences
rlm@572 13 gained from unsupervised free play. The worm is a five-segment
rlm@572 14 creature equipped with touch, proprioception, and muscle tension
rlm@572 15 senses. It recognizes actions using only proprioception data.
rlm@572 16
rlm@572 17 In order to build this empathic, action-recognizing system, I created
rlm@572 18 a program called \texttt{CORTEX}, which is a complete platform for embodied
rlm@572 19 AI research. It provides multiple senses for simulated creatures,
rlm@572 20 including vision, touch, proprioception, muscle tension, and hearing.
rlm@572 21 Each of these senses provides a wealth of parameters that are
rlm@572 22 biologically inspired. \texttt{CORTEX} is able to simulate any number of
rlm@572 23 creatures and senses, and provides facilities for easily modeling and
rlm@572 24 creating new creatures. As a research platform it is more complete
rlm@572 25 than any other system currently available.