Invited Talk: Autonomous Cars that Coordinate with People (Anca Dragan, Berkeley)

Abstract: Cars tend to treat people like obstacles whose motion needs to be anticipated, so that the car can best stay out of their way. This results in ultra-defensive cars that cannot coordinate with people, because they miss on a key aspect of coordination: it's not just the car interpreting and responding to the actions of people, people also interpret and respond to the car's actions. We introduce a mathematical formulation of interaction that accounts for this, and show how learning and optimal control can be leveraged to generate car behavior that results in natural coordination strategies, like the car negotiating a merge or inching forward at an intersection to test whether it can go.

Bio: Anca is an Assistant Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She run the InterACT Lab, where they focus 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. She works across different applications, from assistive robots, to manufacturing, to autonomous cars, and draw from optimal control, planning, estimation, learning, and cognitive science. She also helped fund and serve on the steering committee for the Berkeley AI Research (BAIR) Lab, and am a co-PI of the Center for Human-Compatible AI.