Autonomous cars tend to treat people like obstacles whose motion needs to be anticipated so that the car can best stay out of their way, resulting in ultradefensive cars that can’t coordinate with people. They miss 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.
Anca Dragan introduces a mathematical formulation of interaction that accounts for this and demonstrates how learning and optimal control can be leveraged to generate car behavior that results in natural coordination strategies, such as the car negotiating a merge or inching forward at an intersection to test whether it can go.
Anca Dragan 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. Anca runs the InterACT Lab, where she 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. The lab works across different applications, from assistive robots to manufacturing to autonomous cars, and draws from optimal control, planning, estimation, learning, and cognitive science. Anca also helped found and serves on the steering committee for the Berkeley AI Research (BAIR) Lab and is a co-PI of the Center for Human-Compatible AI.
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