ability to learn is a potentially compelling and important quality for
interactive synthetic characters. My most recent work is focused on developing
a practical approach to real-time learning for synthetic characters. Our
implementation is grounded in the techniques of reinforcement learning
and informed by insights from animal training. It simplifies the learning
task for characters by (a) enabling them to take advantage of predictable
regularities in their world, (b) allowing them to make maximal use of
any supervisory signals, and (c) making them easy to train by humans.
We built an autonomous animated dog that can be trained with a technique
used to train real dogs called ``clicker training''. Capabilities
demonstrated include being trained to recognize and use acoustic
patterns as cues for actions, as well as to synthesize new actions
from novel paths through its motion space.
Video - 4 MB Quicktime