Progress in Symmetry Preserving Robot Control
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Abstract: In this talk, I will present a few recent topics on geometric control that all have one thing in common: exploiting symmetries for solving control problems. Inspired by existing mathematical tools for studying the symmetry structures of geometric spaces, I will present our recent results on trajectory-independent Model Predictive Control (MPC) and cost function design. I will show that with a proper left-invariant metric, setting the gradient of the cost function as the tracking error in the Lie algebra leads to a quadratic Lyapunov function that enables globally exponential convergence. In the end, I will show numerical experiments on tracking control of a 3D rigid body dynamics and implementation of the MPC for pose control and locomotion experiments on a quadrupedal robot MIT Mini Cheetah.
Bio: Maani Ghaffari received the Ph.D. degree from the Centre for Autonomous Systems (CAS), University of Technology Sydney, NSW, Australia, in 2017. He is currently an Assistant Professor at the Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, USA. He recently established the Computational Autonomy and Robotics Laboratory. He is the recipient of the 2021 Amazon Research Awards. His research interests lie in the theory and applications of robotics and autonomous systems.
***Event will take place in hybrid format. The location for in-person attendance will be room 1311 EECS. Attendance will also be possible via Zoom. Zoom link and password will be distributed to the Controls Group e-mail list-serv.
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