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Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion

L5 · ResearcherResearcharXiv· 10/14/2026

Advances the understanding of neural weight spaces for robotics, enabling generative creation of control policies and insights into embodied AI.

AI Summary

Researchers developed a that generates functional humanoid robot control networks directly from weight distributions, revealing consistent kinematic patterns across different methods.

Excerpt

Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, while comparatively little attention has been given to directly modeling the distribution of their learned weights and generating functional networks in weight space. In this work, we investigate whether high-performing humanoid control networks can be generati

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