Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
[Robotyka] Autorzy: Huixuan Cheng, Bike Zhang, Shankar Sastry i 1 więcej
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 generatively modeled directly through their weights. We adopt a latent diffusion framework that couples a linear autoencoder with a Diffusion Transformer. The autoencoder compresses actor network parameters into a compact latent space, and the Diffusion Transformer models the distribution of these latents. Trained on networks collected from reinforcement learning for the Unitree G1 robot, our approach ge…
- Kategoria arxiv
- cs.RO
- Kategoria
- Robotyka