Date
Event Location
Online

Safety-Critical Control with Model Uncertainty:
Robust, Adaptive, and RL based Approaches

Friday, October 30, 2020 | 12:00 PM | Online

Zoom Link: https://msu.zoom.us/j/97383731374

Password: 183655

Robotics and Control Webinar

Koushil Sreenath
Mechanical Engineering
University of California Berkeley

“Biological systems such as birds and humans are able to move with great agility, efficiency, and robustness in a wide range of environments. Endowing machines with similar capabilities requires designing controllers that address the challenges of high-degree-of-freedom, high-degree-of-underactuation, nonlinear & hybrid dynamics, as well as input, state, and safety-critical constraints in the presence of model and sensing uncertainty. In this talk, I will present the design of planning and control algorithms for (i) dynamic legged locomotion over discrete terrain that requires enforcing safety-critical constraints in the form of precise foot placements; and (ii) dynamic aerial manipulation through cooperative transportation of a cable-suspended payload using multiple aerial robots with safety-critical constraints on manifolds. I will show that we can address the challenges of stability of hybrid systems through control Lyapunov functions (CLFs), input and state constraints through CLF-based quadratic programs, and safety-critical constraints through control barrier functions. I will show that robust, adaptive and RL based formulations of control Lyapunov and barrier functions can respectively address effects of model uncertainty on stability and safety while geometric formulations enable constraint enforcement on manifolds.”


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 entitled Adaptive Sampling with Mobile WSN (IET). 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.