arXiv cs.AI· Chao Chen, Zhenrong Wang, Jilong Wang, Yusen Feng, Jiangran Lyu, Li Yi, He Wang·· 13 小时前AI 评分38
HumanoidNDM：用人类神经动力学模型弥合运动跟踪的训练-部署差距
HumanoidNDM: Closing the Training-Deployment Gap of Motion Tracking via Humanoid Neural Dynamics Model
AI 导读
HumanoidNDM 是一个面向人形机器人运动跟踪的在线微调框架，通过真实世界训练的神经动力学模型弥合训练-部署差距。该框架采用低频参考扰动与自适应强度机制采集数据，在欧氏空间训练基于历史的本体感知动力学模型，无需昂贵的动作捕捉系统，并直接通过反向传播模型梯度微调策略。在 Unitree G1 机器人上的物理验证显示，该框架能快速适应各类扰动，是首个无需 MoCap 的人形跟踪策略在线微调框架。
正文 · 原文
Abstract:Humanoid robots have recently achieved remarkable success in motion tracking. However, during real-world deployment, robots inevitably encounter dynamics gaps arising from either extra physical disturbances or inaccurate simulation modeling, resulting in a significant training-deployment gap. Therefore, effectively leveraging real-world data to fine-tune policies represents the crucial last mile for robust deployment. To bridge this gap, we propose HumanoidNDM, an online fine-tuning framework for humanoid motion tracking driven by a real-world-trained neural dynamics model. First, in the data collection stage, we introduce a low-frequency reference perturbation method with an adaptive intensity mechanism, balancing deployment feasibility and exploration efficiency. Second, for the model training stage, we train a robust history-based, proprioceptive neural dynamics model in Euclidean space by incorporating differentiable forward kinematics, eliminating the reliance on costly motion capture systems. Finally, in the policy fine-tuning stage, we directly fine-tune the policy via backpropagated model gradients, achieving high data efficiency. Through physical validation on the Unitree G1 robot, we demonstrate that HumanoidNDM enables rapid real-world adaptation to various perturbations, representing the first online, MoCap-free fine-tuning framework for humanoid tracking policies.
| Comments: | Accepted to CoRL 2026 |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.34126 [cs.RO] |
| (or arXiv:2610.34126v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34126 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chao Chen [view email]
[v1]
Sun, 25 Oct 2026 07:51:22 UTC (11,192 KB)
来源：arXiv cs.AI · arxiv.org