Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning

31 Mar 2018Łukasz KidzińskiSharada P. MohantyCarmichael OngJennifer L. HicksSean F. CarrollSergey LevineMarcel SalathéScott L. Delp

Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing time and costs and improving treatment outcomes. Because of the large and complex solution spaces of biomechanical models, current methods are constrained to specific movements and models, requiring careful design of a controller and hindering many possible applications... (read more)

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