[2604.07084] Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models

Computer Science > Robotics arXiv:2604.07084 (cs) [Submitted on 8 Apr 2026] Title:Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models Authors:Davood Soleymanzadeh, Xiao Liang, Minghui Zheng View a PDF of the paper titled Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models, by Davood Soleymanzadeh and 2 other authors View PDF HTML (experimental) 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 planning. However, many existing methods generate only a single path for a given workspace across different runs, and do not leverage their open-loop structure for inference-time optimization. To address this limitation, we introduce Flow Motion Policy, an open-loop, end-to-end neural motion planner for robotic manipulators that leverages the stochastic generative formulation of flow matching methods to capture the inherent multi-modality of planning datasets. By modeling a distribution over feasible paths, Flow Motion Policy enables efficient inference-time best-of-$N$ sampling. The method generates multiple end-to-end candidate paths, evaluates their collision status after planning, and executes the first collision-free solution. We benchmark the Flow Motion Policy against representative sampling-based and neural motion planning methods....