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MA-SafeDiffuser: Safe Multi-Agent Planning with Diffusion Probabilistic Models

2026-11-03 19:20:32
Kiran Ravish, Ankita Kushwaha, Preeti, Pawan Kumar
arXiv_AI
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2611.05524

PDF

https://arxiv.org/pdf/2611.05524.pdf

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