Claire Tomlin (UC Berkeley)
This talk will be held virtually and will be live streamed on our website. Full participation (including the capacity to ask questions) will be available via Zoom webinar. A link to the Zoom webinar will be shared on this page closer to the event date.
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. We present new methods for computing the reachable set, based on a functional approximation which has the potential to broadly alleviate its computational complexity. In the second part of the talk, 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 robotic platforms at Berkeley, including demonstrations of motion planning around people, and navigating in a priori unknown environments.
If you require accommodation for communication, please contact our Access Coordinator at simonsevents [at] berkeley.edu with as much advance notice as possible.