About the talk:
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 efficient active learning methods to build predictive models of humans's preferences by eliciting comparisons from a mixed set of humans, and further analyzing the generalizability and robustness of the learned human models for safe and seamless interaction with robots.
About the speaker:
Dorsa Sadigh is an assistant professor in Computer Science and Electrical
Engineering at Stanford University. Her research interests lie in the
intersection of robotics, learning and control theory, and algorithmic
human-robot interaction. Specifically, she works on developing efficient
algorithms for autonomous systems that safely and reliably interact
with people. Dorsa has received her doctoral degree in Electrical
Engineering and Computer Sciences (EECS) at UC Berkeley in 2017, and
has received her bachelor's degree in EECS at UC Berkeley in 2012.
She is awarded the NSF and NDSEG graduate research fellowships as well
as Leon O. Chua departmental award, Arthur M. Hopkin departmental award.
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Contact information:
Dorsa Sadigh dorsa@cs.stanford.edu