P3：面向约束扩散控制的持久粒子规划
P3: Persistent Particle Planning for Constrained Diffusion Control
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中文总结 AI 辅助
针对扩散控制中测试时约束的可行性问题，提出持久粒子规划（P3），通过序贯蒙特卡洛重用并细化候选计划群体，减少路径切换，提升成功率并加快规划速度。
中文摘要 AI 辅助
扩散模型为轨迹提供了富有表现力的先验，但将这些先验适应于测试时的约束，需要在连续的控制决策中保持可行性和一致性。我们提出了持久粒子规划（P3），一种用于扩散控制的序贯蒙特卡洛框架，它在重新规划步骤间维护一个加权的候选计划群体。在每个控制步骤中，P3对候选轨迹进行平移和部分重新加噪，在最新观测下对其进行细化，并使用约束感知的加权和重采样在替代延续中进行选择，而无需重新训练扩散模型。我们将去噪和重新规划视为一个Feynman-Kac粒子系统，并在理想化修复下对其进行分析。我们证明了重新加噪深度控制着保留计划在其路径上保持可靠的程度，并且保留一条稀有且分离良好的路径所需的计划数远少于从头采样重新发现它所需的计划数。在多种测试时约束配置下的实验表明，群体重用减少了路径切换，并在无约束违反的情况下提高了成功率。由于P3细化早期计划而非重新绘制它们，它每次重新规划所需的去噪迭代也更少。在迷宫导航任务中，它比重新生成的群体以及通过约束优化修正单个采样计划的方法规划得更快。代码和预训练模型可在该https URL获取。
英文摘要
Diffusion models provide expressive priors over trajectories, but adapting these priors to test-time constraints requires maintaining feasibility and consistency across successive control decisions. We introduce Persistent Particle Planning (P3), a sequential Monte Carlo framework for diffusion control that maintains a weighted population of candidate plans across replanning steps. At each control step, P3 shifts and partially re-noises the candidate trajectories, refines them under the latest observation, and uses constraint-aware weighting and resampling to select among alternative continuations without retraining the diffusion model. We consider denoising and replanning as one Feynman--Kac particle system and analyze it under an idealized repair. We prove that the re-noising depth controls how reliably a kept plan stays on its route, and that keeping a rare, well-separated route takes far fewer plans than rediscovering it by sampling from scratch. Experiments under multiple test-time constraint configurations show that population reuse reduces route switching and improves success without constraint violations. Because P3 refines earlier plans instead of redrawing them, it also needs fewer denoising iterations per replan. On maze-navigation tasks, it plans faster than both regenerated populations and methods that correct a single sampled plan by constrained optimization. Code and pretrained models are available at https://github.com/p3-username/p3-anon.
发表机构
- Bilkent University（比尔肯大学）
机构由 AI 辅助整理，请以论文原文为准。
