外观
论文arXiv 2610.2060614 天前
使用 Diffusion 建模与理解人形机器人策略的神经网络权重
该研究用线性自编码器与 Diffusion Transformer 建模人形控制网络权重，并在 Unitree G1 真机多种动作上验证生成网络。
宇树 G1原文链接：Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion（arXiv 2610.20606 · 发布日期：2026-10-14）
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
该研究用线性自编码器与 Diffusion Transformer 建模人形控制网络权重，并在 Unitree G1 真机多种动作上验证生成网络。
核心要点
- 人形控制网络多由常规训练获得，直接建模其学习权重分布并生成可用网络仍少有研究。
- 方法将 actor 参数经线性自编码器压缩为潜空间，再用 Diffusion Transformer 学习潜变量分布。
- 基于 Unitree G1 强化学习网络训练后，生成网络可在真实世界执行行走、跳舞和旋转踢。
- 权重分析显示第一层权重一致反映机器人运动学先验，且在 RL 与 IL 训练下均出现。
验证范围与局限：验证包括 Unitree G1 真实世界中的行走、跳舞和旋转踢等动作。
英文摘要 (Abstract)
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 generates deployable networks that perform various motions in the real world, including walking, dancing, and spin kick. Beyond generation, we further study the learned weight space to better understand the structure captured by these humanoid control networks. We identify a structural principle in the first-layer weights that consistently reflects the robot's kinematic prior. Notably, this kinematic prior emerges under both Reinforcement Learning (RL) and Imitation Learning (IL) training, suggesting that it is not specific to a particular optimization process but may instead reflect common knowledge associated with humanoid embodiment. Together, these results demonstrate the feasibility of generative modeling in humanoid network weight space, while providing initial insights into the information encoded by their learned weights. Additional results are available on our project website: https://humanoid-weight-diffusion.github.io/
作者：Huixuan Cheng, Bike Zhang, Shankar Sastry, Koushil Sreenath
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