拡散モデルによるマルチロボット動作計画のための軌道レベルモード誘導
Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning
拡散モデルを用いた動作計画において、粗い部分軌道の事前情報を徐々に注入することで、多様性を保ちながら制御可能な軌道生成を実現し、マルチロボット協調にも拡張した研究。
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著者: Tianyou Yu, Shengze Cai, Chao Xu
分類: cs.RO
原文アブストラクト
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.