Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Abstract Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically corr...

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Bibliographic Details
Main Authors: Seungmoon Song, Łukasz Kidziński, Xue Bin Peng, Carmichael Ong, Jennifer Hicks, Sergey Levine, Christopher G. Atkeson, Scott L. Delp
Format: Article
Language:English
Published: BMC 2021-08-01
Series:Journal of NeuroEngineering and Rehabilitation
Subjects:
Neuromechanical simulation
Deep reinforcement learning
Motor control
Locomotion
Biomechanics
Musculoskeletal modeling
Online Access:https://doi.org/10.1186/s12984-021-00919-y

Internet

https://doi.org/10.1186/s12984-021-00919-y

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