Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models

Abstract:Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators. These methods enable planning directly from sensor observations without relying on a privileged collision checker during motion planning. However, existing planners produce a single path for a given planning problem and cannot exploit their open-loop nature to propose multiple motion plans. To address this limitation, we introduce Flow Motion Policy, an open-loop neural motion planner that uses flow matching to generate a batch of motion plan proposals by learning a distribution over motion plans conditioned on the planning observation. At inference time, it samples multiple candidate motion plans to enable efficient best-of-$N$ inference while avoiding iterative collision checking during planning. We benchmark the Flow Motion Policy against representative sampling-based, optimization-based and neural motion planning methods. Evaluation results demonstrate that Flow Motion Policy improves planning success and efficiency, highlighting the effectiveness of stochastic generative policies for end-to-end motion planning and best-of-$N$ sampling. Project website: \href{this https URL}{this https URL}

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2604.07084 [cs.RO]

(or arXiv:2604.07084v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.07084 Focus to learn more arXiv-issued DOI via DataCite

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