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Safety-Critical Control for Dynamic Robots: A Model-based and Data-driven Approach

April 23, 2021 - 12:00pm to 1:00pm

Abstract: Model-based controllers can be designed to provide guarantees on stability and safety for dynamical systems. In this talk, I will show how we can address the challenges of stability through control Lyapunov functions (CLFs), input and state constraints through CLF-based quadratic programs, and safety-critical constraints through control barrier functions (CBFs). However, the performance of model-based controllers is dependent on having a precise model of the system. Model uncertainty could lead not only to poor performance but could also destabilize the system as well as violate safety constraints. I will present recent results on using model-based control along with data-driven methods to address stability and safety for systems with uncertain dynamics. In particular, I will show how reinforcement learning as well as Gaussian process regression can be used along with CLF and CBF-based control to address the adverse effects of model uncertainty.Biography: Koushil Sreenath is an Assistant Professor of Mechanical Engineering, at UC Berkeley. He received a Ph.D. degree in Electrical Engineering and Computer Science and a M.S. degree in Applied Mathematics from the University of Michigan at Ann Arbor, MI, in 2011. He was a Postdoctoral Scholar at the GRASP Lab at University of Pennsylvania from 2011 to 2013 and an Assistant Professor at Carnegie Mellon University from 2013 to 2017. His research interest lies at the intersection of highly dynamic robotics and applied nonlinear control. His work on dynamic legged locomotion was featured on The Discovery Channel, CNN, ESPN, FOX, and CBS. His work on dynamic aerial manipulation was featured on the IEEE Spectrum, New Scientist, and Huffington Post. His work on adaptive sampling with mobile sensor networks was published as a book. He received the NSF CAREER, Hellman Fellow, Best Paper Award at the Robotics: Science and Systems (RSS), and the Google Faculty Research Award in Robotics

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