外观
论文arXiv 2604.173356 个月前
扩散模型驱动宇树G1全身地形自适应运动控制框架
提出结合扩散模型与强化学习的人形机器人全身运动框架，解决传统RL下肢主导及模仿学习缺乏在线适应问题。在宇树G1上实现基于机载感知的目标导向与地形自适应，成功穿越楼梯、障碍等复杂场景。
宇树 G1本文由 AI 根据公开内容整理，可能有误，请以原文为准：Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking（arXiv 2604.17335）
核心内容
针对人形机器人高维控制与地形适应难题，研究提出融合参考动作生成与地形感知适应的全身运动框架。该方法利用重定向人类动作训练扩散模型进行实时参考预测，并同步训练全身参考追踪器。通过在冻结生成器的闭环设置下微调追踪器，提升了非完美参考下的鲁棒性，最终在Unitree G1人形机器人上完成部署验证。
系统支持基于机载感知与计算的方向性目标到达及全身地形自适应。硬件实验表明机器人能稳定穿越箱体、跨栏、楼梯及混合地形。量化结果证实，引入在线运动生成与追踪器微调机制显著增强了策略泛化能力，有效克服了纯奖励塑形RL导致的下肢主导行为及纯模仿学习无法在线适应地形的局限。
关键看点
- 算法架构：结合扩散模型实时生成地形感知参考动作与RL全身追踪器，通过闭环微调提升对生成误差的鲁棒性
- 硬件验证：在Unitree G1人形机器人上实现全机载感知与计算部署，无需外部动捕或离线算力支持
- 场景能力：成功实现方向性目标导向控制，稳定穿越箱体、跨栏、楼梯及多种组合复杂地形
- 性能优势：量化证明在线运动生成与追踪器微调机制相比传统方法显著提升泛化性与地形适应鲁棒度
工程与产业观察
该工作为具身智能提供了“生成式先验+RL适配”的工程范式，解决了人形机器人在非结构化环境中全身协调与实时适应的矛盾。其基于Unitree G1的机载部署验证了扩散模型在端侧运动规划中的可行性，对推动人形机器人从实验室演示走向真实复杂场景作业具有重要参考价值。
英文摘要 (Abstract)
Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception. Directly applying reinforcement learning (RL) with reward shaping to humanoid locomotion often leads to lower-body-dominated behaviors, whereas imitation-based RL can learn more coordinated whole-body skills but is typically limited to replaying reference motions without a mechanism to adapt them online from perception for terrain-aware locomotion. To address this gap, we propose a whole-body humanoid locomotion framework that combines skills learned from reference motions with terrain-aware adaptation. We first train a diffusion model on retargeted human motions for real-time prediction of terrain-aware reference motions. Concurrently, we train a whole-body reference tracker with RL using this motion data. To improve robustness under imperfectly generated references, we further fine-tune the tracker with a frozen motion generator in a closed-loop setting. The resulting system supports directional goal-reaching control with terrain-aware whole-body adaptation, and can be deployed on a Unitree G1 humanoid robot with onboard perception and computation. The hardware experiments demonstrate successful traversal over boxes, hurdles, stairs, and mixed terrain combinations. Quantitative results further show the benefits of incorporating online motion generation and fine-tuning the motion tracker for improved generalization and robustness.
作者：Zewei Zhang, Kehan Wen, Michael Xu, Junzhe He, Chenhao Li, Takahiro Miki, Clemens Schwarke, Chong Zhang, Xue Bin Peng, Marco Hutter
来源
本页事实对应的公开页面，以原文为准。