Scalable Muscle-actuated Human Simulation and Control

Scalable Muscle-actuated Human

Simulation and Control

Seunghwan Lee1     Kyoungmin Lee2     Moonseok Park2    Jehee Lee1

1 Seoul National University    2 Seoul National University Bundang Hosiptial

Physics-based simulation and control of dynamic motor skills actuated by 284 to 346 musculotendon units. (Left to right) Musculoskeletal model with multi-segment feet, two-toe feet, and prosthetic legs.



Many anatomical factors, such as bone geometry and muscle condition, interact to affect human movements. This work aims to build a comprehensive musculoskeletal model and its control system that reproduces realistic human movements driven by muscle contraction dynamics. The variations in the anatomic model generate a spectrum of human movements ranging from typical to highly stylistic movements. To do so, we discuss scalable and reliable simulation of anatomical features, robust control of under-actuated dynamical systems based on deep reinforcement learning, and modeling of pose-dependent joint limits. The key technical contribution is a scalable, two-level imitation learning algorithm that can deal with a comprehensive full-body musculoskeletal model with 346 muscles. We demonstrate the predictive simulation of dynamic motor skills under anatomical conditions including bone deformity, muscle weakness, contracture, and the use of a prosthesis. We also simulate various pathological gaits and predictively visualize how orthopedic surgeries improve post-operative gaits.


Seunghwan Lee, Kyoungmin Lee, Moonseok Park, and Jehee Lee,
Scalable Muscle-actuated Human Simulation and Control
ACM Transactions on Graphics (SIGGRAPH 2019), Volume 37, Article 73

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