{"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/one-shot-reinforcement-learning-for-robot","title":"One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay","arxiv_id":"1711.10137","date":"2017-11-28","proceeding":null,"authors":["Jake Bruce","Niko Suenderhauf","Piotr Mirowski","Raia Hadsell","Michael Milford"],"abstract":"Recently, model-free reinforcement learning algorithms have been shown to\nsolve challenging problems by learning from extensive interaction with the\nenvironment. A significant issue with transferring this success to the robotics\ndomain is that interaction with the real world is costly, but training on\nlimited experience is prone to overfitting. We present a method for learning to\nnavigate, to a fixed goal and in a known environment, on a mobile robot. The\nrobot leverages an interactive world model built from a single traversal of the\nenvironment, a pre-trained visual feature encoder, and stochastic environmental\naugmentation, to demonstrate successful zero-shot transfer under real-world\nenvironmental variations without fine-tuning.","url_abs":"http://arxiv.org/abs/1711.10137v2","url_pdf":"http://arxiv.org/pdf/1711.10137v2.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":"one-shot-reinforcement-learning-for-robot","repo_url":"https://github.com/ayusefi/Localization-Papers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}