用拓扑图引导扩散模型，让机器人路径更安全可行。
Graph-Guided Safe Diffuser: Topological Graph Guidance for Safe Diffusion Planning
- 高层拓扑图抽象环境结构，指导底层扩散模型生成轨迹
- 在Maze2D中碰撞率从40-50%降至0%，成功率升至98%
- 适合需要高安全性的运动规划场景，如机器人导航
许多基于扩散的规划器通过推理时引导来保证安全，但这种穿插的轨迹变形常因流形破裂导致运动学不可行。本文提出图引导安全扩散器（G2SD），一个分层框架：高层拓扑图规划器抽象数据流形为学习到的潜在图，进行高层规划；底层扩散模型生成连续轨迹，并以高层规划选出的图节点表征为条件。理论分析揭示了扩散规划中流形破裂的条件，并证明随着分段数增加，G2SD能降低约束违反概率。实验显示，G2SD显著优于基线，在Maze2D导航中将无碰撞目标达成率从40-50%提升至98%，并在运动任务中取得更优评分。
原文摘要 · Abstract (English)
Many diffusion-based planners enforce safety through inference-time guidance, but such interleaved trajectory deformations often degrade kinematic feasibility due to manifold rupture. We propose Graph-Guided Safe Diffuser (G2SD), a hierarchical framework that leverages a high-level topological graph planner to guide a low-level diffusion model. G2SD enforces safety at a structural level by abstracting the data manifold into a learned latent graph, on which high-level planning is performed. Continuous trajectories are generated by diffusion planners, which are conditioned on the graph node representations selected by the high-level planner. Theoretical analyses demonstrate conditions under which manifold rupture occurs in diffusion planners, and show that G2SD improves safety by reducing the constraint violation probability as the number of segments increases. Experiments demonstrate that G2SD substantially outperforms baselines, increasing goal-reaching rate without any collision from 40-50% to 98% in Maze2D navigation and also achieving superior task scores in locomotion.
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