視覚移動ポリシーのゼロショット地形汎化
Zero-Shot Terrain Generalization for Visual Locomotion Policies
3D LiDARの生入力を直接用いるマルチタスク強化学習で脚式ロボットの移動制御器を学習し、階段や不整地など13種類の未知環境でゼロショット汎化を実現した。
著者: Alejandro Escontrela, George Yu, Peng Xu, Atil Iscen, Jie Tan
分類: cs.RO
原文アブストラクト
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.
