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Modeling and control of the human musculoskeletal system is important forunderstanding human motion, developing embodied intelligence, and optimizinghuman-robot interaction systems. However, current open-source models arerestricted to a limited range of body parts and often with a reduced number ofmuscles. There is also a lack of algorithms capable of controlling over 600muscles to generate reasonable human movements. To fill this gap, we build acomprehensive musculoskeletal model with 90 body segments, 206 joints, and 700muscle-tendon units, allowing simulation of full-body dynamics and interactionwith various devices. We develop a new algorithm using low-dimensionalrepresentation and hierarchical deep reinforcement learning to achievestate-of-the-art full-body control. We validate the effectiveness of our modeland algorithm in simulations and on real human locomotion data. Themusculoskeletal model, along with its control algorithm, will be made availableto the research community to promote a deeper understanding of human motioncontrol and better design of interactive robots.

Self Model for Embodied Intelligence: Modeling Full-Body Human  Musculoskeletal System and Locomotion Control with Hierarchical  Low-Dimensional Representation