[Submitted on 3 Nov 2026]
Title:MA-SafeDiffuser: Safe Multi-Agent Planning with Diffusion Probabilistic Models
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Abstract:We propose MA-SafeDiffuser, a multi-agent extension of SafeDiffuser that equips diffusion-based trajectory planners with finite-time diffusion invariance guarantees for joint-agent safety specifications. Building on the single-agent construction that embeds control barrier function (CBF) constraints into reverse-diffusion updates, we: (i) formalize joint safe sets as intersections of per-agent and pairwise barrier sets; (ii) derive centralized and decentralized (communication-aware) constrained denoising procedures with provable invariance under mild assumptions; (iii) address local-trap and deadlock phenomena via time-varying specifications and liveness CBFs; and (iv) develop a lightweight benchmarking suite including a multi-agent Maze2D domain. Empirically, MA-SafeDiffuser reduces violation counts relative to unconstrained diffusion baselines, while retaining planning quality. We provide algorithms, proofs, and reference implementations.
Comments: Accepted at AAMAS 2026. 14 pages, including supplementary material
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2611.05524 [cs.AI]
(or arXiv:2611.05524v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.05524
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.65109/PAPW1165
DOI(s) linking to related resources
Submission history
From: Kiran Ravish [view email] [v1] Tue, 3 Nov 2026 19:20:32 UTC (3,466 KB)
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