実時間足元地形再構成を伴う歩容適応型知覚ヒューマノイド歩行
Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
足元を向いた深度カメラとU-Netでリアルタイムに自己中心的高さマップを再構成し、知覚と歩容位相・全身制御を単一の強化学習ポリシーに統合することで、階段やギャップを含む複雑地形でのヒューマノイド歩行を実現した。
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著者: Haolin Song, Hongbo Zhu, Tao Yu, Yan Liu, Mingqi Yuan, Wengang Zhou, Hua Chen, Houqiang Li
分類: cs.RO
原文アブストラクト
For full-size humanoid robots, even with recent advances in reinforcement learning-based control, achieving reliable locomotion on complex terrains, such as long staircases, remains challenging. In such settings, limited perception, ambiguous terrain cues, and insufficient adaptation of gait timing can cause even a single misplaced or mistimed step to result in rapid loss of balance. We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy. A downward-facing depth camera mounted under the base observes the support region around the feet, and a compact U-Net reconstructs a dense egocentric height map from each frame in real time, operating at the same frequency as the control loop. The perceptual height map, together with proprioceptive observations, is processed by a unified policy that produces joint commands and a global stepping-phase signal, allowing gait timing and whole-body posture to be adapted jointly to the commanded motion and local terrain geometry. We further adopt a single-stage successive teacher-student training scheme for efficient policy learning and knowledge transfer. Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings, including forward and backward stair ascent and descent, as well as crossing a 46 cm gap. Project Page:https://ga-phl.github.io/
