Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations

2026-09-17

AI Summary

为解决三维密集障碍物环境中的类人机器人行走问题，提出了一种基于虚拟现实演示的学习框架MTC，并通过场景感知运动重定向算法生成无碰撞轨迹。

Abstract

While learning from human motions has enabled highly dynamic humanoid skills such as dancing and martial arts in obstacle-free space, traversal through densely cluttered environments remains underexplored. These spaces are three-dimensional and geometrically constrained, requiring scene-aware locomotion that tightly couples whole-body motion with scene geometry for obstacle avoidance. To address these challenges, we present Moving Through Clutter (MTC), a learning-from-demonstration framework for scene-aware humanoid locomotion. To bypass costly physical scene construction, MTC uses procedurally generated Virtual Reality environments for immersive data collection. To transform these human motions into training-ready humanoid motions, we propose a scene-aware motion retargeting algorithm that converts human demonstrations into humanoid trajectories while strictly enforcing robot-scene clearance to guarantee collision-free traversal. These reference trajectories are then used to train a scene-aware locomotion policy that deploys on a Unitree G1 humanoid. Evaluated on our proposed MTC-Challenge for multi-obstacle traversal, the policy demonstrates a 70.2% collision-free rate across diverse scenarios, successfully traversing complex environments through diverse whole-body skills, including crawling through low-clearance passages and squeezing through narrow gaps.

Problem

• scene-aware locomotion
• cluttered environments
• humanoid robots
• obstacle avoidance

Innovation

• scene-aware locomotion
• motion retargeting
• virtual reality