ICRA 2011 Paper Abstract


Paper TuP203.5

Yi, Seung Joon (University of Pennsylvania), Zhang, Byoung-Tak (Seoul National University), Hong, Dennis (Virginia Tech), Lee, Daniel D. (University of Pennsylvania)

Learning Full Body Push Recovery Control for Small Humanoid Robots

Scheduled for presentation during the Regular Sessions "Humanoid Robots II" (TuP203), Tuesday, May 10, 2011, 16:25−16:40, Room 3D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Humanoid and Bipedal Locomotion, Humanoid Robots, Learning and Adaptive Systems


Dynamic bipedal walking is susceptible to external disturbances and surface irregularities, requiring robust feedback control to remain stable. In this work, we present a practical hierarchical push recovery strategy that can be readily implemented on a wide range of humanoid robots. Our method consists of low level controllers that perform simple, biomechanically motivated push recovery actions and a high level controller that combines the low level controllers according to proprioceptive and inertial sensory signals and the current robot state. Reinforcement learning is used to optimize the parameters of the controllers in order to maximize the stability of the robot over a broad range of external disturbances. The controllers are learned on a physical simulation and implemented on the Darwin-HP humanoid robot platform, and the resulting experiments demonstrate effective full body push recovery behaviors during dynamic walking.



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