Mechanical and Civil Engineering Seminar
Learning in Human-Robot Interaction: Optimization as an Inductive Bias
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, as well as ways in which robots can remain flexible to human behavior that violates it.
Contact: Carolina Oseguera at 626-395-4271 email@example.com