Title:
Mathematics and Learning for Agile and Dynamic Bipedal Locomotion

dc.contributor.author Grizzle, Jessy W.
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename University of Michigan. Department of Electrical Engineering and Computer Science en_US
dc.date.accessioned 2018-10-09T20:34:38Z
dc.date.available 2018-10-09T20:34:38Z
dc.date.issued 2018-09-26
dc.description Presented on September 26, 2018 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech. en_US
dc.description Jessy W. Grizzle received a Ph.D. in electrical engineering from The University of Texas at Austin in 1983. He is currently a professor of Electrical Engineering and Computer Science at the University of Michigan, where he holds the titles of the Elmer Gilbert Distinguished University Professor and the Jerry and Carol Levin Professor of Engineering. Grizzle jointly holds sixteen patents dealing with emissions reduction in passenger vehicles through improved control system design. A fellow of the IEEE and IFAC, he received the Paper of the Year Award from the IEEE Vehicular Technology Society in 1993, the George S. Axelby Award in 2002, the Control Systems Technology Award in 2003, the Bode Prize in 2012, and the IEEE Transactions on Control Systems Technology Outstanding Paper Award in 2014. His work on bipedal locomotion has been the object of numerous plenary lectures and has been featured in The Economist, Wired Magazine, Discover Magazine, Scientific American, Popular Mechanics, and several television programs, including CNN, ESPN, and the Discovery Channel. en_US
dc.description Runtime: 76:45 minutes en_US
dc.description.abstract Is it great fortune or a curse to do legged robotics on a university campus that has Maya Lin’s earthen sculpture, The Wave Field? Come to the talk and find out! Our work on model-based feedback control for highly dynamic locomotion in bipedal robots will be amply illustrated through images, videos, and math. The core technical portion of the presentation is a method to overcome the obstructions imposed by high-dimensional bipedal models by embedding a stable walking motion in an attractive low-dimensional surface of the system’s state space. The process begins with trajectory optimization to design an open-loop periodic walking motion of the high-dimensional model and then adding to this solution, a carefully selected set of additional open-loop trajectories of the model that steer toward the nominal motion. A drawback of trajectories is that they provide little information on how to respond to a disturbance. To address this shortcoming, supervised machine learning is used to extract a low-dimensional, state-variable realization of the open-loop trajectories. The periodic orbit is now an attractor of a low-dimensional state-variable model but is not attractive in the full-order system. We then use the special structure of mechanical models associated with bipedal robots to embed the low-dimensional model in the original model in such a manner that the desired walking motions are locally exponentially stable. When combined with robot vision, we hope this approach to control design will allow the full complexity of the Wave Field to be conquered. In any case, as Jovanotti points out, “Non c'è scommessa più persa di quella che non giocherò.” The speaker for one will keep trying! en_US
dc.format.extent 76:45 minutes
dc.identifier.uri http://hdl.handle.net/1853/60468
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries IRIM Seminar Series
dc.subject Robotics en_US
dc.title Mathematics and Learning for Agile and Dynamic Bipedal Locomotion en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries IRIM Seminar Series
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication 9bcc24f0-cb07-4df8-9acb-94b7b80c1e46
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