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. My research is about algorithm design for these autonomous and intelligent systems that interact with people.
Today, I plan to talk about: humans, interactions, and societal implications of interactions. I will first discuss our recent results on active learning of humans’ preferences for robotics tasks. We develop data efficient techniques that learn computational models of humans’ preferences and compare our method with learning from demonstration. I will then formalize interactive autonomy, and our approach in design of learning and control algorithms that influence humans’ actions for better safety and coordination. Finally, I will discuss our approach on studying societal implications of autonomous systems. Specifically, I will talk about routing and decision making algorithms for autonomous cars that reduce congestion on mixed-autonomy roads. Hosts: Nancy Lynch & Sertac Karaman.
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 Amazon Faculty Research Award, the NSF and NDSEG graduate research fellowships as well as the Leon O. Chua departmental award.