<ul><li>Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions.</li><li>Coordinating multiple agents in a shared environment poses challenges in continuous spaces, where traditional optimization algorithms struggle with scalability.</li><li>Diffusion models have shown promise in single-agent path planning, but extending them to MAPF introduces new challenges for constraint feasibility, such as inter-agent collision avoidance.</li><li>To address this, a novel approach is proposed that combines constrained optimization with diffusion models for MAPF in continuous spaces, resulting in feasible multi-agent trajectories that respect collision avoidance and kinematic constraints.</li></ul>

Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models

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