{"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/virtual-to-real-deep-reinforcement-learning","title":"Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation","arxiv_id":"1703.00420","date":"2017-03-01","proceeding":null,"authors":["Lei Tai","Giuseppe Paolo","Ming Liu"],"abstract":"We present a learning-based mapless motion planner by taking the sparse\n10-dimensional range findings and the target position with respect to the\nmobile robot coordinate frame as input and the continuous steering commands as\noutput. Traditional motion planners for mobile ground robots with a laser range\nsensor mostly depend on the obstacle map of the navigation environment where\nboth the highly precise laser sensor and the obstacle map building work of the\nenvironment are indispensable. We show that, through an asynchronous deep\nreinforcement learning method, a mapless motion planner can be trained\nend-to-end without any manually designed features and prior demonstrations. The\ntrained planner can be directly applied in unseen virtual and real\nenvironments. The experiments show that the proposed mapless motion planner can\nnavigate the nonholonomic mobile robot to the desired targets without colliding\nwith any obstacles.","url_abs":"http://arxiv.org/abs/1703.00420v4","url_pdf":"http://arxiv.org/pdf/1703.00420v4.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":"virtual-to-real-deep-reinforcement-learning","repo_url":"https://github.com/hamidthri/navbot_ppo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"virtual-to-real-deep-reinforcement-learning","repo_url":"https://github.com/m5823779/DDPG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"virtual-to-real-deep-reinforcement-learning","repo_url":"https://github.com/m5823779/MotionPlannerUsingDDPG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":null,"task_name":"Position"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"continuous-control","task_name":"continuous-control"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.00420","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}