Distributed Model-Based Diffusion: Finite Horizon Contraction under Bounded Delay
arXiv:2608.27685v1 Announce Type: new Abstract: Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimensionality. A collection of interacting aerial vehicles or self-driving cars in an intersection are examples of complex multi-agent systems that remain difficult to solve without many simplifying assumptions. The presence of communication latency between agents further increases the difficulty. In this paper, we analyze Distributed Model-Based Diffusion: a sampling-based Model-Predictive Control method suitable for highly nonlinear, nonconvex, nonsmooth, multi-agent systems. We prove contraction and robustness to latency for multi-agent, nonconvex problems, showing applicability to real-world constraints. We test the algorithm on a circleswap task, a cooperative medium-fidelity driving task, and in an aerial combat scenario. Despite the addition of latency, our algorithm improves circleswap makespan by 31% and increases aerial combat win rate by 25% compared to centralized Model-Based Diffusion.
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[Submitted on 27 Aug 2026]
Title:Distributed Model-Based Diffusion: Finite Horizon Contraction under Bounded Delay
View a PDF of the paper titled Distributed Model-Based Diffusion: Finite Horizon Contraction under Bounded Delay, by Seth Golembeski and 4 other authors
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Abstract:Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimensionality. A collection of interacting aerial vehicles or self-driving cars in an intersection are examples of complex multi-agent systems that remain difficult to solve without many simplifying assumptions. The presence of communication latency between agents further increases the difficulty. In this paper, we analyze Distributed Model-Based Diffusion: a sampling-based Model-Predictive Control method suitable for highly nonlinear, nonconvex, nonsmooth, multi-agent systems. We prove contraction and robustness to latency for multi-agent, nonconvex problems, showing applicability to real-world constraints. We test the algorithm on a circleswap task, a cooperative medium-fidelity driving task, and in an aerial combat scenario. Despite the addition of latency, our algorithm improves circleswap makespan by 31% and increases aerial combat win rate by 25% compared to centralized Model-Based Diffusion.
Comments: 8 pages, 3 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.27685 [cs.RO]
(or arXiv:2608.27685v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.27685
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Seth Golembeski [view email] [v1] Thu, 27 Aug 2026 20:16:41 UTC (789 KB)
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