轨迹级模态引导的可控扩散多机器人运动规划

中文摘要 AI 辅助

运动规划通常存在多个可行解，这使得多模态生成对于灵活的多机器人协调尤为重要。扩散模型天然能够学习此类轨迹分布，然而在不限制生成的前提下融入粗略且部分的轨迹先验仍然具有挑战性。此类先验指示的是解空间中的期望区域而非单一解，这促使我们采用保留多模态性的条件生成方法。本文在干净的轨迹空间中对轨迹生成进行引导，并通过依赖于时间步的引导强度逐步融入轨迹先验。在每个反向扩散步骤中，重建的干净轨迹为整合规划代价和部分轨迹先验提供了统一空间。规划代价通过基于梯度的细化来融入，而部分先验则在相应的噪声水平下以递减的引导强度逐步注入。这在前阶段将生成引导至先验区域，同时逐步释放约束以保留扩散模型固有的多模态性。该框架通过引入机器人间碰撞代价自然扩展到多机器人规划。在单机器人和多机器人规划任务上的实验展示了可控的轨迹合成、多样的可行解以及安全的多智能体协调。

英文摘要

Motion planning often admits multiple feasible solutions, making multimodal generation valuable, particularly for flexible multi-robot coordination. Diffusion models naturally learn such trajectory distributions, yet incorporating coarse and partial trajectory priors without restricting generation remains challenging. Such priors indicate a desirable region of the solution space rather than a single solution, motivating conditioned generation that preserves multimodality. In this paper, we guide trajectory generation in the clean trajectory space and progressively incorporate trajectory priors with a timestep-dependent guidance strength. At each reverse diffusion step, the reconstructed clean trajectory provides a unified space for integrating planning costs and partial trajectory priors. Planning costs are incorporated through gradient-based refinement, while the partial prior is progressively injected at the corresponding noise levels with decreasing guidance strength. This guides generation toward the prior in early stages while gradually releasing the constraint to preserve the inherent multimodality of the diffusion model. The framework naturally extends to multi-robot planning by incorporating inter-robot collision costs. Experiments on single- and multi-robot planning tasks demonstrate controllable trajectory synthesis, diverse feasible solutions, and safe multi-agent coordination.

发表机构

• Zhejiang University（浙江大学）

机构由 AI 辅助整理，请以论文原文为准。