Robot Locomotion Group
In this talk, I will present nonlinear feedback control synthesis algorithms which combine randomized motion planning algorithms, popular in robotics, with nonlinear stability and safety verification using sums-of-squares optimization. In order to drive the system to a goal state or limit cycle, the algorithm systematically populates the controllable subset of state space with feedback controllers verified over local regions, or "funnels"; we have now developed efficient methods for computing these funnels along trajectories and around limit cycles, on systems with hybrid dynamics, on systems with mixed polynomial/trigonometric nonlinearities, and for stochastic systems. Under mild assumptions, theses planning algorithms can probabilistically converge to a controller which stabilizes the entire controllable set; experiments suggest that this coverage occurs relatively quickly.
By exploiting advances in both randomized motion planning and optimization-based control verification and synthesis, these algorithms provide the potential for applying rigorous control ideas on "hopelessly nonconvex" problems in robotics. I'll describe the application of these ideas to bipedal locomotion, quadrupedal locomotion over rough terrain, and small unmanned airplanes that dart through forests and land on a perch.
Russ Tedrake is an Associate Professor of Electrical Engineering and Computer Science and Aeronautics and Astronautics at Massachusetts Institute of Technology (USA), and a member of the Computer Science and Artificial Intelligence Lab. He received his B.S.E. in Computer Engineering from the University of Michigan, Ann Arbor, in 1999, his Ph.D. in Electrical Engineering and Computer Science from MIT in 2004, and was a Postdoctoral Associate in Brain and Cognitive Sciences at MIT before joining the faculty in EECS in 2005. He has received an NSF CAREER award, the MIT Jerome Saltzer award for undergraduate teaching, the DARPA Young Faculty Award, and was named a Microsoft Research New Faculty Fellow. The goal of his research is to build robots which exploit their natural dynamics to achieve extraordinary agility and efficiency.