Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

Song, Seungmoon; Kidzinski, Lukasz; Peng, Xue Bin; Ong, Carmichael; Hicks, Jennifer; Levine, Sergey; Atkeson, Christopher G; Delp, Scott L

Song, Seungmoon; Kidzinski, Lukasz; Peng, Xue Bin; Ong, Carmichael; Hicks, Jennifer; Levine, Sergey; Atkeson, Christopher G; Delp, Scott L.

Afiliação

Song S; Department of Mechanical Engineering, Stanford University, Stanford, CA, USA. smsong@stanford.edu.
Kidzinski L; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Peng XB; Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
Ong C; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Hicks J; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Levine S; Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
Atkeson CG; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Delp SL; Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

J Neuroeng Rehabil ; 18(1): 126, 2021 08 16.

Article em En | MEDLINE | ID: mdl-34399772

Assuntos

Locomoção; Reforço Psicológico; Fenômenos Biomecânicos; Simulação por Computador; Humanos; Caminhada

Palavras-chave

Academic competition; Biomechanics; Deep reinforcement learning; Locomotion; Motor control; Musculoskeletal modeling; Neuromechanical simulation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reforço Psicológico / Locomoção Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Neuroeng Rehabil Ano de publicação: 2021 Tipo de documento: Article

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