{"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/artificial-intelligence-for-prosthetics","title":"Artificial Intelligence for Prosthetics - challenge solutions","arxiv_id":"1902.02441","date":"2019-02-07","proceeding":null,"authors":["Łukasz Kidziński","Carmichael Ong","Sharada Prasanna Mohanty","Jennifer Hicks","Sean F. Carroll","Bo Zhou","Hongsheng Zeng","Fan Wang","Rongzhong Lian","Hao Tian","Wojciech Jaśkowski","Garrett Andersen","Odd Rune Lykkebø","Nihat Engin Toklu","Pranav Shyam","Rupesh Kumar Srivastava","Sergey Kolesnikov","Oleksii Hrinchuk","Anton Pechenko","Mattias Ljungström","Zhen Wang","Xu Hu","Zehong Hu","Minghui Qiu","Jun Huang","Aleksei Shpilman","Ivan Sosin","Oleg Svidchenko","Aleksandra Malysheva","Daniel Kudenko","Lance Rane","Aditya Bhatt","Zhengfei Wang","Penghui Qi","Zeyang Yu","Peng Peng","Quan Yuan","Wenxin Li","Yunsheng Tian","Ruihan Yang","Pingchuan Ma","Shauharda Khadka","Somdeb Majumdar","Zach Dwiel","Yinyin Liu","Evren Tumer","Jeremy Watson","Marcel Salathé","Sergey Levine","Scott Delp"],"abstract":"In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge,\nparticipants were tasked with building a controller for a musculoskeletal model\nwith a goal of matching a given time-varying velocity vector. Top participants\nwere invited to describe their algorithms. In this work, we describe the\nchallenge and present thirteen solutions that used deep reinforcement learning\napproaches. Many solutions use similar relaxations and heuristics, such as\nreward shaping, frame skipping, discretization of the action space, symmetry,\nand policy blending. However, each team implemented different modifications of\nthe known algorithms by, for example, dividing the task into subtasks, learning\nlow-level control, or by incorporating expert knowledge and using imitation\nlearning.","url_abs":"http://arxiv.org/abs/1902.02441v1","url_pdf":"http://arxiv.org/pdf/1902.02441v1.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":"artificial-intelligence-for-prosthetics","repo_url":"https://github.com/iasawseen/MultiServerRL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"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=1902.02441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.02441"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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