Introduces a scalable reinforcement learning framework that enables high-fidelity control of a whole-body human musculoskeletal system with over 700 muscles.
April 1, 2026
Original Paper
Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity
arXiv · 2603.29332
The Takeaway
Modeling human movement at this complexity was previously intractable due to the curse of dimensionality in control and rewards. This work allows for realistic simulation of dynamic tasks like dancing and backflips, providing a new foundation for biomechanical research and high-fidelity embodied AI.
From the abstract
The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the high-dimensional, over-actuated system. Forward imitation approaches based on deep reinforcement learning exhibited inadequate tracking perfor