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In this work, we develop an automated method to generate 3D human walkingmotion in simulation which is comparable to real-world human motion. At thecore, our work leverages the ability of deep reinforcement learning methods tolearn high-dimensional motor skills while being robust to variations in theenvironment dynamics. Our approach iterates between policy learning andparameter identification to match the real-world bio-mechanical human data. Wepresent a thorough evaluation of the kinematics, kinetics and ground reactionforces generated by our learned virtual human agent. We also show that themethod generalizes well across human-subjects with different kinematicstructure and gait-characteristics.

Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning