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arXiv · 2011.05513

Zero-Shot Terrain Generalization for Visual Locomotion Policies

Alejandro Escontrela·George Yu·Peng Xu·Atil Iscen·Jie Tan
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Abstract

Legged robots have unparalleled mobility on unstructured terrains. However, it remains an open challenge to design locomotion controllers that can operate in a large variety of environments. In this paper, we address this challenge of automatically learning locomotion controllers that can generalize to a diverse collection of terrains often encountered in the real world. We frame this challenge as a multi-task reinforcement learning problem and define each task as a type of terrain that the robot needs to traverse. We propose an end-to-end learning approach that makes direct use of the raw exteroceptive inputs gathered from a simulated 3D LiDAR sensor, thus circumventing the need for ground-truth heightmaps or preprocessing of perception information. As a result, the learned controller demonstrates excellent zero-shot generalization capabilities and can navigate 13 different environments, including stairs, rugged land, cluttered offices, and indoor spaces with humans.

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Alejandro Escontrela, George Yu, Peng Xu, Atil Iscen, Jie Tan. 2020-11-11. Zero-Shot Terrain Generalization for Visual Locomotion Policies. https://arxiv.org/abs/2011.05513

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