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Today’s society is rapidly advancing towards robotics systems that interact and collaborate with humans, e.g., semi-autonomous vehicles interacting with drivers and pedestrians, medical robots used in collaboration with doctors, or service robots interacting with their users in smart homes. In this talk, I will first discuss interactive autonomy, where we develop algorithms for autonomous systems that influence humans, and further leverage these effects for better safety, efficiency, coordination, and estimation. I will then focus on our recent results to efficiently and actively learning predictive models of humans’ preferences by eliciting comparisons from a mixed set of humans, and further discuss how to analyze the generalizability and robustness of the learned human models for safe and seamless interaction with robots.
Dorsa Sadigh is an Assistant Professor in the Computer Science Department and Electrical Engineering Department at Stanford University. Her work is focused on the design of algorithms for autonomous systems that safely and reliably interact with people.
