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• W3136290172 abstract "In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the ability of deep reinforcement learning methods to learn high-dimensional motor skills while being robust to variations in the environment dynamics. Our approach iterates between policy learning and parameter identification to match the real-world bio-mechanical human data. We present a thorough evaluation of the kinematics, kinetics and ground reaction forces generated by our learned virtual human agent. We also show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics." @default.
• W3136290172 created "2021-03-29" @default.
• W3136290172 creator A5073584166 @default.
• W3136290172 date "2021-03-15" @default.
• W3136290172 modified "2023-09-26" @default.
• W3136290172 title "Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning." @default.
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• W3136290172 hasPublicationYear "2021" @default.
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