Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework using constraint-aware diffusion models (CADM) to first simulate neighbors' future trajectories and then plan own trajectories under safety constraints. SID enables a minimal communication scheme triggered only in congested scenarios and outperforms baselines, scaling to 108 robots and 160 obstacles.
Article intelligence
Key points
- SID uses CADM to simulate neighbor trajectories for decentralized collision avoidance
- Minimal communication scheme coordinates only when necessary
- Outperforms baselines and scales to large scenarios
Why it matters
This matters because SID uses CADM to simulate neighbor trajectories for decentralized collision avoidance.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27697] Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
[Submitted on 26 May 2026]
Title:Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
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Abstract:Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing or reliable communication. However, most existing planners, whether classical or learning-based, generate trajectories from a static snapshot of the local observation, which limits their ability to anticipate the future behavior of neighboring robots. This limitation is critical as the number of robots increases and the environment becomes more cluttered. To overcome this challenge, this paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models (CADM). SID first uses CADM to simulate the future trajectories of neighboring robots from their currently observed states, and then uses the same CADM to plan each robot's own trajectory under safety constraints informed by these simulations. Crucially, the accurate simulation of neighbors enables a minimal communication scheme that triggers coordination only when necessary in highly congested scenarios. Experiments across diverse environments show that SID consistently outperforms baseline methods in terms of planning effectiveness and constraint satisfaction, and scales to scenarios with 108 robots and 160 obstacles.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.27697 [cs.RO]
(or arXiv:2605.27697v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.27697
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jinhao Liang [view email] [v1] Tue, 26 May 2026 21:17:53 UTC (4,080 KB)
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