Here I demonstrate the power of using embodied artificial intelligence
to attack the \emph{action recognition} problem, which is the challenge of
recognizing actions performed by a creature given limited data about
the creature's actions, such as a video recording. I solve this
problem in the case of a worm-like creature performing actions such as
curling and wiggling. 

To attack the action recognition problem, I developed a computational
model of empathy (\texttt{EMPATH}) which allows me to recognize actions using
simple, embodied representations of actions (which require rich
sensory data), even when that sensory data is not actually available.
The missing sense data is imagined by combining previous experiences
gained from unsupervised free play. The worm is a five-segment
creature equipped with touch, proprioception, and muscle tension
senses. It recognizes actions using only proprioception data.

In order to build this empathic, action-recognizing system, I created
a program called \texttt{CORTEX}, which is a complete platform for embodied
AI research. It provides multiple senses for simulated creatures,
including vision, touch, proprioception, muscle tension, and hearing.
Each of these senses provides a wealth of parameters that are
biologically inspired. \texttt{CORTEX} is able to simulate any number of
creatures and senses, and provides facilities for easily modeling and
creating new creatures. As a research platform it is more complete
than any other system currently available.
