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• W3104464522 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." @default.
• W3104464522 created "2020-11-23" @default.
• W3104464522 creator A5015686392 @default.
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• W3104464522 date "2020-11-10" @default.
• W3104464522 modified "2023-09-26" @default.
• W3104464522 title "Zero-Shot Terrain Generalization for Visual Locomotion Policies" @default.
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• W3104464522 doi "https://doi.org/10.48550/arxiv.2011.05513" @default.
• W3104464522 hasPublicationYear "2020" @default.
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