{"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/learning-human-behaviors-from-motion-capture","title":"Learning human behaviors from motion capture by adversarial imitation","arxiv_id":"1707.02201","date":"2017-07-07","proceeding":null,"authors":["Josh Merel","Yuval Tassa","Dhruva TB","Sriram Srinivasan","Jay Lemmon","Ziyu Wang","Greg Wayne","Nicolas Heess"],"abstract":"Rapid progress in deep reinforcement learning has made it increasingly\nfeasible to train controllers for high-dimensional humanoid bodies. However,\nmethods that use pure reinforcement learning with simple reward functions tend\nto produce non-humanlike and overly stereotyped movement behaviors. In this\nwork, we extend generative adversarial imitation learning to enable training of\ngeneric neural network policies to produce humanlike movement patterns from\nlimited demonstrations consisting only of partially observed state features,\nwithout access to actions, even when the demonstrations come from a body with\ndifferent and unknown physical parameters. We leverage this approach to build\nsub-skill policies from motion capture data and show that they can be reused to\nsolve tasks when controlled by a higher level controller.","url_abs":"http://arxiv.org/abs/1707.02201v2","url_pdf":"http://arxiv.org/pdf/1707.02201v2.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":"learning-human-behaviors-from-motion-capture","repo_url":"https://github.com/ywchao/merel-mocap-gail","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.02201","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}