{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/motion-planning-networks","title":"Motion Planning Networks","arxiv_id":"1806.05767","date":"2018-06-14","proceeding":null,"authors":["Ahmed H. Qureshi","Anthony Simeonov","Mayur J. Bency","Michael C. Yip"],"abstract":"Fast and efficient motion planning algorithms are crucial for many\nstate-of-the-art robotics applications such as self-driving cars. Existing\nmotion planning methods become ineffective as their computational complexity\nincreases exponentially with the dimensionality of the motion planning problem.\nTo address this issue, we present Motion Planning Networks (MPNet), a neural\nnetwork-based novel planning algorithm. The proposed method encodes the given\nworkspaces directly from a point cloud measurement and generates the end-to-end\ncollision-free paths for the given start and goal configurations. We evaluate\nMPNet on various 2D and 3D environments including the planning of a 7 DOF\nBaxter robot manipulator. The results show that MPNet is not only consistently\ncomputationally efficient in all environments but also generalizes to\ncompletely unseen environments. The results also show that the computation time\nof MPNet consistently remains less than 1 second in all presented experiments,\nwhich is significantly lower than existing state-of-the-art motion planning\nalgorithms.","url_abs":"http://arxiv.org/abs/1806.05767v2","url_pdf":"http://arxiv.org/pdf/1806.05767v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"motion-planning-networks","repo_url":"https://github.com/ahq1993/MPNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.05767","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}