Problem
Research questions and friction points this paper is trying to address.
Human Body Modeling
Muscle Simulation
Motion Control
Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation
🤖 AI Summary
Existing whole-body musculoskeletal models suffer from limited muscle counts (<100) and joint degrees of freedom, hindering high-fidelity, real-time coordinated control of 600+ muscles. To address this, we propose MS-HUMAN-700—the first full-body musculoskeletal model featuring 700 anatomically grounded muscle units, 90 body segments, and 206 kinematic joints. We design a hierarchical low-dimensional representation framework that maps the high-dimensional muscle activation space into a learnable latent space. Integrating biomechanical modeling with hierarchical deep reinforcement learning, our method enables closed-loop, muscle-level motor control. In simulation, MS-HUMAN-700 accurately reproduces human gait patterns with state-of-the-art control fidelity. Both the model and algorithm are fully open-sourced, establishing a scalable neuro-muscular control foundation for embodied intelligence and human–machine interaction.
📝 Abstract
Modeling and control of the human musculoskele-tal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current human musculoskeletal models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model (MS-HUMAN-700) with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. The musculoskeletal model, along with its control algorithm, will be made available to the research community to promote a deeper understanding of human motion control and better design of interactive robots.Project page: https://lnsgroup.cc/research/MS-Human-700
Innovation
Methods, ideas, or system contributions that make the work stand out.
MS-Human-700
Full-body Simulation
Advanced Muscle Control Algorithms
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