# 从沉浸式人类示范中学习穿越3D杂乱环境的场景感知人形机器人运动

> 原文链接：https://robotics.hellobike.com/papers/2026-09-17-2609-21107
> 频道：papers | 更新日期：2026-09-17 | 标签：arxiv, cs.RO
> 核心摘要：MTC通过VR示范采集与场景感知重定向学习人形机器人避障运动，并在Unitree G1和MTC-Challenge上验证。

# 从沉浸式人类示范中学习穿越3D杂乱环境的场景感知人形机器人运动

::: v-pre
> 原文链接：[Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations](https://arxiv.org/abs/2609.21107)（arXiv 2609.21107 · 发布日期：2026-09-17）

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

MTC通过VR示范采集与场景感知重定向学习人形机器人避障运动，并在Unitree G1和MTC-Challenge上验证。

## 核心要点

- 密集杂乱环境具有三维几何约束，需要全身运动与场景几何紧耦合以避障。
- MTC在程序生成的VR环境中采集沉浸式人类示范，避免昂贵的实体场景搭建。
- 场景感知重定向将示范转为人形轨迹，并严格保持机器人与场景间隙以无碰穿越。
- 策略部署于Unitree G1，在MTC-Challenge多障碍穿越中达到70.2%无碰率。

**验证范围与局限**：验证范围为Unitree G1上的部署及MTC-Challenge中的多障碍穿越场景。

## 英文摘要 (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.

作者：Beichen Wang, Tong Xu, Daniel Kosukhin, Yuen-Hei Yeung, Yuanjie Lu, Xuesu Xiao
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## 原始参考来源 (Sources)

- [arXiv 2609.21107：Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations](https://arxiv.org/abs/2609.21107)