aiqbench analysis
This paper introduces Model-Based Diffusion Optimal Control (MDOC), a novel framework that integrates diffusion models with optimal control to solve multi-robot motion planning without relying on large demonstration datasets. By combining dynamics models with Control Barrier Function constraints, the method rigorously enforces safety and dynamic feasibility while scaling effectively through Conflict-Based Search. The approach demonstrates superior sample efficiency and computational speed compared to existing probabilistic inference baselines in continuous environments.
Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
arXiv:2607.12423v2 Announce Type: replace Abstract: Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth of the joint trajectory space and the difficulty of enforcing dynamic feasibility and hard safety constraints. Recent approaches recast trajectory planning as probabilistic inference, sampling from a posterior over trajectories using diffusion models whose score functions are learned from demonstration data. While showing promising performance, these approaches are limited: they often rely on sizable demonstration datasets and struggle to rigorously enforce dynamics and hard safety constraints during sampling. To this end, we introduce Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data. Crucially, we show that MDOC's safety mechanism -- combining known dynamics models with Control Barrier Function-constrained projections -- naturally scales to multi-robot planning settings through Conflict-Based Search. Across simulation experiments, this integrated method consistently outperforms representative baseline planners in sample efficiency, geometric smoothness, and success rate, while reducing computation time and producing collision-free trajectories.
Abstract:Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth of the joint trajectory space and the difficulty of enforcing dynamic feasibility and hard safety constraints. Recent approaches recast trajectory planning as probabilistic inference, sampling from a posterior over trajectories using diffusion models whose score functions are learned from demonstration data. While showing promising performance, these approaches are limited: they often rely on sizable demonstration datasets and struggle to rigorously enforce dynamics and hard safety constraints during sampling. To this end, we introduce Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data. Crucially, we show that MDOC's safety mechanism -- combining known dynamics models with Control Barrier Function-constrained projections -- naturally scales to multi-robot planning settings through Conflict-Based Search. Across simulation experiments, this integrated method consistently outperforms representative baseline planners in sample efficiency, geometric smoothness, and success rate, while reducing computation time and producing collision-free trajectories.
Robotics (cs.RO)
arXiv:2607.12423 [cs.RO]
(or arXiv:2607.12423v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.12423 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.15607/RSS.2026.XXII.041 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 268 words
