让人形机器人在复杂地形上稳定行走并执行多样动作
PGMT: Perceptive General Motion Tracking for Humanoid Robots
- 通过感知地形信息动态调整运动参考，实现自适应跟踪
- 可在37厘米高障碍物上零样本部署，支持摔倒恢复
- 适合需要复杂环境移动与远程操控的人形机器人
人形机器人运动追踪系统可复现多种全身动作，但在复杂地形上因忽略地形特征导致动作不可行。本文提出PGMT（Perceptive General Motion Tracking），通过独立选择的动作参考与地形数据学习地形适应能力。该方法先建立通用追踪与恢复先验，再利用运动条件化的地形片段，仅编码当前动作相关的地形区域。结合地形感知的追踪松弛机制，允许合理偏离参考轨迹以维持动作意图。在真实世界中对Unitree G1进行零样本部署，实现了对高达37厘米障碍物的鲁棒地形适应行走与全身动作执行，支持远程操控、动态运动追踪及跌倒恢复。PGMT将通用人形运动追踪扩展至非平坦地面，统一实现了地形适应性行走、多样化全身行为与远程操控。
原文摘要 · Abstract (English)
Humanoid motion trackers can reproduce diverse whole-body motions, but their performance degrades on complex terrain where terrain-agnostic references become physically infeasible. We present PGMT, a Perceptive General Motion Tracking pipeline for humanoid robots that learns terrain adaptation from independently selected motion references and terrains. PGMT first learns a general tracking and recovery prior, then incorporates terrain perception through motion-conditioned terrain glimpses that selectively encode regions relevant to the current motion. Terrain-aware tracking relaxation allows necessary deviations from the reference while preserving its motion intent. Zero-shot deployment on a Unitree G1 demonstrates robust terrain-adaptive locomotion and whole-body motion execution over real-world terrain with obstacles up to 37 cm high, while supporting teleoperation, dynamic motion tracking, and fall recovery. PGMT extends general humanoid motion tracking beyond flat ground, providing a unified policy for terrain-adaptive locomotion, diverse whole-body behaviors, and teleoperation in complex environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。