Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads

This is the submission video for the 2022 ICRA (IEEE International Conference on Robotics and Automation) paper “Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads“ by Jeremy Dao, Kevin Green, Helei Duan, Alan Fern, Jonathan Hurst Preprint link to full paper: Abstract: Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipedal locomotion work, have not considered locomotion under a variety of external loads that can significantly influence the overall system dynamics. In many applications, robots will need to maintain robust locomotion under a wide range of potential dynamic loads, such as pulling a cart or carrying a large container of sloshing liquid, ideally without requiring additional load-sensing capabilities. In this work, we explore the capabilities of reinforcement learning (RL) and sim-to-real transfer for bipedal locom