Safe Learning in Robotics
Seminar | October 24 | 4-5 p.m. | 1011 Evans Hall
Claire Tomlin, UC Berkeley
A great deal of research in recent years has focused on robot learning. In many applications, guarantees that specifications are satisfied throughout the learning process are paramount. For the safety specification, we present a controller synthesis technique based on the computation of reachable sets, using optimal control and game theory. In the first part of the talk, we will review these methods and their application to collision avoidance and avionics design in air traffic management systems, and networks of unmanned aerial vehicles. In the second part, we will present a toolbox of methods combining reachability with data-driven techniques inspired by machine learning, to enable performance improvement while maintaining safety. We will illustrate these safe learning methods on a quadrotor UAV experimental platform which we have at Berkeley, including demonstrations of motion planning around people.