{"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/transfer-learning-for-prosthetics-using","title":"Transfer Learning for Prosthetics Using Imitation Learning","arxiv_id":"1901.04772","date":"2019-01-15","proceeding":null,"authors":["Montaser Mohammedalamen","Waleed D. Khamies","Benjamin Rosman"],"abstract":"In this paper, We Apply Reinforcement learning (RL) techniques to train a\nrealistic biomechanical model to work with different people and on different\nwalking environments. We benchmarking 3 RL algorithms: Deep Deterministic\nPolicy Gradient (DDPG), Trust Region Policy Optimization (TRPO) and Proximal\nPolicy Optimization (PPO) in OpenSim environment, Also we apply imitation\nlearning to a prosthetics domain to reduce the training time needed to design\ncustomized prosthetics. We use DDPG algorithm to train an original expert\nagent. We then propose a modification to the Dataset Aggregation (DAgger)\nalgorithm to reuse the expert knowledge and train a new target agent to\nreplicate that behaviour in fewer than 5 iterations, compared to the 100\niterations taken by the expert agent which means reducing training time by 95%.\nOur modifications to the DAgger algorithm improve the balance between\nexploiting the expert policy and exploring the environment. We show empirically\nthat these improve convergence time of the target agent, particularly when\nthere is some degree of variation between expert and naive agent.","url_abs":"http://arxiv.org/abs/1901.04772v1","url_pdf":"http://arxiv.org/pdf/1901.04772v1.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":"transfer-learning-for-prosthetics-using","repo_url":"https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"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":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"ddpg","method_name":"DDPG"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"experience-replay","method_name":"Experience Replay"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}