Learning to Walk

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. We then incrementally add a navigation layer to the robot from onboard cameras and tightly couple it with locomotion via proprioception without retraining the walking policy. This is enabled by the use of additional safety monitors which are trained in simulation to predict the safe walking speed for the robot under varying conditions and also detect collisions which might get missed by the onboard cameras. The planner then uses these to plan a path for the robot in a locomotion aware way. You can see our robot walking at https://www.youtube.com/watch?v=nBy1piJrq1A (opens in new tab).

Speaker Details

Jitendra Malik is Arthur J. Chick Professor of EECS at UC Berkeley. He obtained his B.Tech degree in EE from IIT Kanpur in 1980 and a PhD in Computer Science from Stanford University in 1985. His research has spanned computer vision, machine learning, modeling of human vision, computer graphics, and most recently robotics. He has advised more than 70 PhD 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 US National Academy of Sciences, the US National Academy of Engineering, and the American Academy of Arts and Sciences.