English(EN) HumanoidNDM: Closing the Training-Deployment Gap of Motion Tracking via Humanoid Neural Dynamics Model

新框架HumanoidNDM弥合机器人训练-部署差距

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了HumanoidNDM，一个旨在弥合人形机器人运动跟踪训练与实际部署之间差距的新型框架。该系统利用在欧几里得空间中训练的神经动力学模型，无需昂贵的动作捕捉系统。该框架包括一种用于高效数据收集的参考扰动方法，以及一个利用反向传播模型梯度实现高数据效率的策略微调阶段。在Unitree G1机器人上的物理验证表明，HumanoidNDM能够快速适应各种实际扰动。 AI

影响通过缩小训练-部署差距，能够更稳健、更高效地将人形机器人在实际场景中部署。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chao Chen, Zhenrong Wang, Jilong Wang, Yusen Feng, Jiangran Lyu, Li Yi, He Wang · 2026-10-27 04:00

HumanoidNDM：通过人形神经动力学模型缩小运动跟踪的训练-部署差距

arXiv:2610.34126v1 Announce Type: cross 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 simulatio…

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HumanoidNDM：通过人形神经动力学模型缩小运动跟踪的训练-部署差距

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