动态分配去噪步数，让机器人在保证成功率的前提下提速两倍以上。
D3P: Dynamic Denoising Diffusion Policy via Reinforcement Learning
- 根据动作重要性动态调整去噪步数，提升推理效率。
- 模拟环境平均提速2.2倍，物理机器人提速1.9倍，成功率不变。
- 适合对实时性要求高的机器人视觉运动任务部署。
扩散策略在学习机器人视觉运动任务的复杂动作分布方面表现优异，但其迭代去噪过程成为实时部署的主要瓶颈。现有加速方法对每个动作固定使用相同去噪步数，隐含假设所有动作重要性相同。我们实验发现，机器人任务中存在对任务成功影响显著的‘关键动作’和常规‘例行动作’。为此，提出动态去噪扩散策略（D3P），在测试时自适应分配去噪步数。D3P通过轻量级状态感知适配器，为每个动作分配最优去噪步数，并通过强化学习联合优化适配器与基础扩散策略，在任务性能与推理效率间取得平衡。在模拟任务中，D3P平均比基线提速2.2倍且成功率无下降；在真实机器人上实现1.9倍加速。
原文摘要 · Abstract (English)
Diffusion policies excel at learning complex action distributions for robotic visuomotor tasks, yet their iterative denoising process poses a major bottleneck for real-time deployment. Existing acceleration methods apply a fixed number of denoising steps per action, implicitly treating all actions as equally important. However, our experiments reveal that robotic tasks often contain a mix of \emph{crucial} and \emph{routine} actions, which differ in their impact on task success. Motivated by this finding, we propose \textbf{D}ynamic \textbf{D}enoising \textbf{D}iffusion \textbf{P}olicy \textbf{(D3P)}, a diffusion-based policy that adaptively allocates denoising steps across actions at test time. D3P uses a lightweight, state-aware adaptor to allocate the optimal number of denoising steps for each action. We jointly optimize the adaptor and base diffusion policy via reinforcement learning to balance task performance and inference efficiency. On simulated tasks, D3P achieves an averaged 2.2$\times$ inference speed-up over baselines without degrading success. Furthermore, we demonstrate D3P's effectiveness on a physical robot, achieving a 1.9$\times$ acceleration over the baseline.
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