Multi-agent motion planningExpert
Training-Free Diffusion Planning with Analytical Local Scores
This arXiv preprint introduces TFDP, a training-free 2D multi-agent path planner that replaces learned diffusion scores with analytical local scores for smoothness, obstacle avoidance, velocity, and inter-agent separation. On standard benchmarks it reaches 96–100% success while averaging 0.59 s per plan versus 88.23 s for the strongest diffusion baseline, and it scales to 300 agents and 100 obstacles in 5.39 s on an A100 GPU. The work matters because it retains diffusion-style iterative refinement without needing trajectory datasets or map-specific training.