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Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning

2021-07-07 00:34:23
Taehei Kim, Sung-Hee Lee

Abstract

Legged robots need to be capable of walking on diverse terrain conditions. In this paper, we present a novel reinforcement learning framework for learning locomotion on non-rigid dynamic terrains. Specifically, our framework can generate quadruped locomotion on flat elastic terrain that consists of a matrix of tiles moving up and down passively when pushed by the robot's feet. A trained robot with 55cm base length can walk on terrain that can sink up to 5cm. We propose a set of observation and reward terms that enable this locomotion; in which we found that it is crucial to include the end-effector history and end-effector velocity terms into observation. We show the effectiveness of our method by training the robot with various terrain conditions.

Abstract (translated)

URL

https://arxiv.org/abs/2107.02955

PDF

https://arxiv.org/pdf/2107.02955.pdf


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