Problem
Research questions and friction points this paper is trying to address.
humanoid robots
motion tracking
complex terrain
terrain adaptation
PGMT: Perceptive General Motion Tracking for Humanoid Robots
🤖 AI Summary
针对复杂地形下人形机器人运动跟踪性能下降的问题，提出PGMT方法，通过学习地形适应性并结合地形感知来实现鲁棒的全地形运动。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Perceptive General Motion Tracking
terrain adaptation
motion-conditioned terrain glimpses
terrain-aware tracking relaxation
zero-shot deployment
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