Related papers: Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild

Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild

URL: http://arxiv.org/abs/2304.10888v3
Date: Fri, 6 Oct 2023 14:17:13 GMT
Title: Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild
Authors: Yikai Wang, Zheyuan Jiang, Jianyu Chen
Abstract summary: We propose a new framework for learning robust, agile and natural legged locomotion skills over challenging terrain. Empirical results on both simulation and real world of a quadruped robot demonstrate that our proposed algorithm enables robustly traversing challenging terrains.
Score: 17.336553501547282
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, reinforcement learning has become a promising and polular solution for robot legged locomotion. Compared to model-based control, reinforcement learning based controllers can achieve better robustness against uncertainties of environments through sim-to-real learning. However, the corresponding learned gaits are in general overly conservative and unatural. In this paper, we propose a new framework for learning robust, agile and natural legged locomotion skills over challenging terrain. We incorporate an adversarial training branch based on real animal locomotion data upon a teacher-student training pipeline for robust sim-to-real transfer. Empirical results on both simulation and real world of a quadruped robot demonstrate that our proposed algorithm enables robustly traversing challenging terrains such as stairs, rocky ground and slippery floor with only proprioceptive perception. Meanwhile, the gaits are more agile, natural, and energy efficient compared to the baselines. Both qualitative and quantitative results are presented in this paper.

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