Training-free diffusion method generates fast multi-agent paths
Training-Free Diffusion Planning with Analytical Local Scores
RoboticsMachine Learning
Summary
Finding smooth and safe paths for many robots in complex spaces is hard, especially when you don’t have example paths to learn from. The authors show a way to plan these paths without any training data by using a mathematical formula that measures how good each part of a path is. This approach changes just small segments at a time but still produces smooth, safe, and goal-directed routes for many robots quickly. It works well even when planning for hundreds of robots around many obstacles.
What this means in practice
- •For robotics engineers: Generate real-time collision-free paths for large groups of robots without needing large training datasets.
- •For drone fleet operators: Coordinate smooth and safe flight trajectories for hundreds of drones navigating cluttered environments quickly and efficiently.
Authors
Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto
Abstract
Path finding and multi-robot motion planning require trajectories that are smooth, goal-directed, and collision-free in environments with complex geometric constraints. Recent diffusion-based planners have shown that trajectory generation can be cast as iterative denoising which has opened the doors to learning-based approaches that can handle multi-modal trajectory distributions and refine entire trajectories. However, a key limitation is that diffusion planners require training on large collections of feasible trajectories, rendering them map-specific, and difficult to deploy when high-quality demonstrations are unavailable. This paper introduces a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. The proposed idea relies on a key observation: the score of a trajectory can be reconstructed by considering only local interactions between neighboring waypoints and nearby constraints. This structure exploitation yields a decomposed denoising procedure that retains the optimization structure of classical trajectory methods while inheriting the iterative refinement behavior of diffusion models. Experiments on a large collection of complex environments and large multi-agent planning tasks show that the proposed analytical score produces smooth and feasible trajectories within limited computational costs, for example in generating feasible paths for 300+ agents in environments containing 100+ obstacles in under 6 seconds on a GPU, outperforming strong learning-based and optimization baselines, while avoiding the data requirements of learned diffusion planners.