提出扩散策略闭环稳定性的理论边界，实现快速实时控制。
Theoretical Closed-loop Stability Bounds for Dynamical System Coupled with Diffusion Policies
- 部分去噪后并行执行动作，降低计算延迟。
- 给出控制器稳定性的数学判据，依赖示范方差。
- 适用于需要快速决策的机器人操控场景。
扩散策略在随机扰动下表现出色，因其能建模多模态动作分布。然而，其依赖计算量大的逆时序扩散（去噪）过程进行动作推断，难以用于需快速决策的实时应用。本文研究仅部分执行去噪过程即执行动作的可行性，允许系统动态与计算机上的反向扩散过程并行演化。传统扩散策略中，系统动态缓慢且两者解耦；本文分析了二者耦合时闭环系统的稳定性理论边界。贡献在于提供一种加速模仿学习的框架，并给出基于示范方差判断控制器是否稳定的度量标准。
原文摘要 · Abstract (English)
Diffusion Policy has shown great performance in robotic manipulation tasks under stochastic perturbations, due to its ability to model multimodal action distributions. Nonetheless, its reliance on a computationally expensive reverse-time diffusion (denoising) process, for action inference, makes it challenging to use for real-time applications where quick decision-making is mandatory. This work studies the possibility of conducting the denoising process only partially before executing an action, allowing the plant to evolve according to its dynamics in parallel to the reverse-time diffusion dynamics ongoing on the computer. In a classical diffusion policy setting, the plant dynamics are usually slow and the two dynamical processes are uncoupled. Here, we investigate theoretical bounds on the stability of closed-loop systems using diffusion policies when the plant dynamics and the denoising dynamics are coupled. The contribution of this work gives a framework for faster imitation learning and a metric that yields if a controller will be stable based on the variance of the demonstrations.
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