Predicting how the human motor control system adapts to new conditions during gait is a grand challenge in biomechanics. Computational models that emulate human motor control could assist in many applications, such as improving surgical planning for gait pathologies and designing devices to restore mobility for lower-limb amputees. Deep reinforcement learning is a promising approach for modeling motor control and its adaptation to new conditions, but it has not been widely explored in biomechanics research. In this webinar, Lukasz Kidzinski from Stanford University provides an introduction to reinforcement learning and highlights its use for developing control strategies for biomechanical applications.
A copy of the webinar slides are available for download at [ Ссылка ]. The osim-rl environment for easily applying reinforcement learning to musculoskeletal models can be accessed at [ Ссылка ]. To learn more about the “AI for Prosthetics” challenge mentioned in the webinar, visit [ Ссылка ].
