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HumanoidNDM: Closing the training-deployment gap in humanoid motion tracking with a neural dynamics model

The study proposes HumanoidNDM, an online fine-tuning framework for humanoid motion tracking driven by a neural dynamics model trained on real robots, aimed at closing the dynamics gap between training and deployment. The method includes low-frequency reference perturbation data collection with an adaptive intensity mechanism, training a dynamics model based on proprioceptive history in Euclidean space combined with differentiable forward kinematics, and directly fine-tuning the policy through backpropagated model gradients.

Part of the event: HumanoidNDM: An Online Fine-Tuning Framework for Humanoid Motion Tracking

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