Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models

[2604.07084] Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2604.07084 (cs) [Submitted on 8 Apr 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] 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 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 } Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: 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 Submission history From: Minghui Zheng [ view email ] [v1] Wed, 8 Apr 2026 13:38:58 UTC (2,497 KB) [v2] Sat, 15 Aug 2026 03:06:35 UTC (2,333 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation#1143816
• History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots#1061294
• Training and Agentic Inference Strategies for LLM-based Manim Animation Generation#120559
• CNCGEN: A Dataset and Framework for Machining Process Planning and Toolpath Generation from B-rep Models#1144905
• ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts#1013709