Trajectory-Level Mode Guidance for Controllable Diffusion-Based Multi-Robot Motion Planning

2026-09-28

AI Summary

This study addresses the challenge in multi-robot planning of reconciling the incorporation of coarse-grained trajectory priors with the preservation of solution multimodality. To this end, it proposes a timestep-dependent, trajectory-level guidance mechanism based on diffusion models. Specifically, priors are progressively injected into the clean trajectory space during reverse generation, with guidance strength dynamically adjusted, while gradient descent is employed to jointly optimize planning costs and collision constraints. This approach enables controllable multi-agent trajectory synthesis, ensuring collision avoidance safety while generating diverse and feasible multimodal planning solutions.

Abstract

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.

Problem

• multi-robot motion planning
• diffusion models
• trajectory priors
• multimodal generation
• controllable trajectory synthesis

Innovation

• Diffusion Models
• Multi-Robot Motion Planning
• Trajectory-Level Guidance
• Multimodal Generation
• Time-Dependent Guidance