提出加速版扩散策略，实现机器人实时控制
Efficient Task-specific Conditional Diffusion Policies: Shortcut Model Acceleration and SO(3) Optimization
- 用快捷路径+无分类器引导提升扩散模型推理速度
- 推理速度比DDIM快近5倍，任务表现保持不变
- 在旋转空间SO(3)上优化，提升动作控制精度
模仿学习，特别是基于扩散策略的方法，近年来在具身智能中备受关注，因其能通过学习预测噪声高效生成动作策略。然而，传统扩散策略依赖迭代去噪，导致推理效率低、响应慢，难以满足机器人实时控制需求。为此，我们提出无分类器快捷扩散策略（CF-SDP），融合无分类器引导与快捷路径加速机制，实现高效的任务特定动作生成，显著提升推理速度。同时，我们将扩散建模扩展至SO(3)流形，在其切空间中定义前向与反向过程，并采用各向同性高斯分布，确保旋转估计稳定准确，增强基于扩散的控制效果。实验表明，该方法在推理速度上相比基于DDIM的扩散策略实现近5倍加速，同时在RoboTwin仿真平台及多种真实场景任务中均表现出优越性能。
原文摘要 · Abstract (English)
Imitation learning, particularly Diffusion Policies based methods, has recently gained significant traction in embodied AI as a powerful approach to action policy generation. These models efficiently generate action policies by learning to predict noise. However, conventional Diffusion Policy methods rely on iterative denoising, leading to inefficient inference and slow response times, which hinder real-time robot control. To address these limitations, we propose a Classifier-Free Shortcut Diffusion Policy (CF-SDP) that integrates classifier-free guidance with shortcut-based acceleration, enabling efficient task-specific action generation while significantly improving inference speed. Furthermore, we extend diffusion modeling to the SO(3) manifold in shortcut model, defining the forward and reverse processes in its tangent space with an isotropic Gaussian distribution. This ensures stable and accurate rotational estimation, enhancing the effectiveness of diffusion-based control. Our approach achieves nearly 5x acceleration in diffusion inference compared to DDIM-based Diffusion Policy while maintaining task performance. Evaluations both on the RoboTwin simulation platform and real-world scenarios across various tasks demonstrate the superiority of our method.
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