Walking the Boundary of Learning and Interaction

Monday, May 3, 2021 - 11:30am to 12:30pm

Event Calendar Category

CSAIL

Speaker Name

Dorsa Sadigh

Affiliation

Stanford

Zoom meeting id

980150

Join Zoom meeting

https://mit.zoom.us/j/91991490406

Abstract

There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans, and how interactions can enable more effective robot learning. This introduces an opportunity for developing new robot learning algorithms that can help advance interactive autonomy. In this talk, I will discuss a formalism for human-robot interaction built upon ideas from representation learning. This formalism provides an orthogonal perspective to theory of mind, and provides a path for scalable partner modeling. Specifically, I will first discuss the notion of latent strategies — low dimensional representations sufficient for capturing non-stationary interactions. I will then talk about some of the challenges of learning such representations when interacting with humans, and how we can develop data-efficient techniques that enable actively learning computational models of human behavior from interaction data: demonstrations, preferences, or physical corrections. Finally, I will wrap up by discussing some of the challenges that arise when considering long-term repeated interactions, and how partner-specific conventions can be leveraged for fast adaptation on new collaborative tasks. 

Biography

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. Specifically, she is interested in developing algorithms for safe and adaptive human-robot interaction. Dorsa has received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and has received her bachelor’s degree in EECS from UC Berkeley in 2012.  She is awarded the NSF CAREER award, the AFOSR Young Investigator award, the IEEE TCCPS early career award, the Google Faculty Award, and the Amazon Faculty Research Award.​