Estimation, planning, control, and learning are giving us robots that can generate good behavior given a specified objective and set of constraints. What I care about is how humans enter this behavior generation picture, and study two complementary challenges: 1) how to optimize behavior when the robot is not acting in isolation, but needs to coordinate or collaborate with people; and 2) what to optimize in order to get the behavior we want. My work has traditionally focused on the former, but more recently I have been casting the latter as a human-robot collaboration problem as well (where the human is the end-user, or even the robotics engineer building the system). Treating it as such has enabled us to use robot actions to gain information; to account for human pedagogic behavior; and to exchange information between the human and the robot via a plethora of communication channels, from external forces that the person physically applies to the robot, to comparison queries, to defining a proxy objective function.
Anca Dragan at the podium. (Photo by J. Dugan)
The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features Berkeley faculty doing visionary research that illustrates the character of the ongoing data revolution. This lecture series is offered to engage our diverse campus community and enrich active connections among colleagues. All campus community members are welcome and encouraged to attend. Arrive at 3:30 PM for light refreshments and discussion prior to the formal presentation.
Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, which focuses on algorithms for human-robot interaction - algorithms that move beyond the robot's function in isolation, and generate robot behavior that also accounts for interaction and coordination with end-users. InterACT work across different applications - from assistive robots, to manufacturing, to autonomous cars - and draws from optimal control, planning, estimation, learning, and cognitive science. She also helped found and serves on the steering committee for the Berkeley AI Research (BAIR) Lab, and she is a co-PI of the Center for Human-Compatible AI. She's been honored by the Sloan Fellowship, MIT TR35, the Okawa award, and an NSF CAREER award.