Learning to Walk with Rapid Motor Adaptation

Monday, October 25, 2021 - 4:00pm to 5:00pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

Jitendra Malik



Event Recording

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Legged locomotion is commonly studied and programmed as a discrete set of structured gait patterns, like walk, trot, gallop.  However, studies of children learning to walk (Adolph et al) show that real-world locomotion is often quite unstructured and more like "bouts of intermittent steps". We have developed a general approach to walking which is built on learning on varied terrains in simulation and then fast online adaptation (fractions of a second) in the real world. This is made possible by our  Rapid Motor Adaptation (RMA) algorithm. RMA consists of two components: a base policy and an adaptation module, both of which can be trained in simulation.  We thus learn walking policies that are much more flexible and adaptable. In our set-up gaits emerge as a consequence of minimizing energy consumption at different target speeds, consistent with various animal motor studies. You can see our robot walking at https://www.youtube.com/watch?v=nBy1piJrq1A


Jitendra Malik is Arthur J. Chick Professor of EECS at UC Berkeley. and Research Scientist Director at Facebook AI Research. His research has spanned computer vision, machine learning, modeling of human vision, computer graphics, and most recently robotics. He has advised more than 70 Ph.D. students and postdocs, many of whom are now prominent researchers. His honors include numerous best paper prizes, the 2013 Distinguished Researcher award in computer vision, the 2016 ACM/AAAI Allen Newell Award, the 2018 IJCAI Award for Research Excellence in AI, and the 2019 IEEE Computer Society’s Computer Pioneer Award for “leading role in developing Computer Vision into a thriving discipline through pioneering research, leadership, and mentorship”. He is a member of the National Academy of Sciences,  the National Academy of Engineering, and the American Academy of Arts and Sciences.