Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning

2021/07/07 by Taehei Kim, Kim, Taehei, Sung‐Hee Lee +2
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Motion and Animation #Robotic Locomotion and Control #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.AI #cs.RO

paper · pdf · doi:10.48550/arxiv.2107.02955

arxiv created 2021/07/07 · openalex publication_date 2021/07/07 · arxiv updated 2021/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Learning agile and dynamic motor skills for legged robots
• Data Efficient Reinforcement Learning for Legged Robots
• Learning to Walk via Deep Reinforcement Learning
• Circus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs

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