GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model

• URL: http://arxiv.org/abs/2104.09771v2
• Date: Thu, 22 Apr 2021 03:40:44 GMT
• Title: GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model
• Authors: Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel van de Panne
• Abstract summary: We show how model-free reinforcement learning can be effectively used with a centroidal model to generate robust control policies for quadrupedal locomotion. We show the potential of the method by demonstrating stepping-stone locomotion, two-legged in-place balance, balance beam locomotion, and sim-to-real transfer without further adaptations.
• Score: 18.66472547798549
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-free reinforcement learning (RL) for legged locomotion commonly relies on a physics simulator that can accurately predict the behaviors of every degree of freedom of the robot. In contrast, approximate reduced-order models are often sufficient for many model-based control strategies. In this work we explore how RL can be effectively used with a centroidal model to generate robust control policies for quadrupedal locomotion. Advantages over RL with a full-order model include a simple reward structure, reduced computational costs, and robust sim-to-real transfer. We further show the potential of the method by demonstrating stepping-stone locomotion, two-legged in-place balance, balance beam locomotion, and sim-to-real transfer without further adaptations. Additional Results: https://www.pair.toronto.edu/glide-quadruped/.

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