外观
论文arXiv 2610.0600223 天前
P3：用于约束扩散控制的持久粒子规划
原文链接：P3: Persistent Particle Planning for Constrained Diffusion Control（arXiv 2610.06002 · 发布日期：2026-10-05）
P3: Persistent Particle Planning for Constrained Diffusion Control
P3以序贯蒙特卡洛复用候选轨迹，在多种测试时约束和迷宫导航任务上验证了更快且更成功。
核心要点
- 扩散轨迹先验在测试时适配约束，需要连续控制决策保持可行性与一致性。
- P3跨重规划步骤维护加权候选计划群，平移、部分重加噪并按约束重采样。
- 分析表明重加噪深度影响保留路线可靠性，保留稀有路线所需计划更少。
- 实验显示复用群体减少路线切换、提高成功率且无约束违反；代码和预训练模型已提供。
验证范围与局限：验证范围包括多种测试时约束配置和迷宫导航任务。
英文摘要 (Abstract)
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.
作者：Hikmet Simsir, Mahyar Fardinfar, Ozgur S. Oguz
来源
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