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New framework HumanoidNDM bridges robot training-deployment gap

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HumanoidNDM, a novel framework designed to bridge the gap between training and real-world deployment for humanoid robot motion tracking. This system utilizes a neural dynamics model trained in Euclidean space, eliminating the need for expensive motion capture systems. The framework includes a reference perturbation method for efficient data collection and a policy fine-tuning stage that leverages backpropagated model gradients for high data efficiency. Physical validation on the Unitree G1 robot demonstrated HumanoidNDM's capability for rapid adaptation to various real-world perturbations. AI

IMPACT Enables more robust and efficient deployment of humanoid robots in real-world scenarios by reducing the training-deployment gap.

RANK_REASON The cluster contains a research paper detailing a new framework for humanoid robot motion tracking. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework HumanoidNDM bridges robot training-deployment gap

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The cluster contains a research paper detailing a new framework for humanoid robot motion tracking. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chao Chen, Zhenrong Wang, Jilong Wang, Yusen Feng, Jiangran Lyu, Li Yi, He Wang ·

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

    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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