The 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.

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