An Optimization-Based Theory of Mind for Human-Robot Interaction

Опубликовано 13 мая 2019, 22:38

Generating robot action for interaction with people is not scalable without learning, but learning from scratch has too high sample complexity. Inductive bias becomes critical, but what is the right inductive bias when it comes to people? We study the assumption that people are driven by intentions and are approximately rational in pursuing them. We derive algorithms that can leverage this assumption, ways in which to bring it closer to matching real human behavior, as well as ways in which robots can remain flexible to human behavior that violates it.

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