[2603.29332] Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity

Computer Science > Robotics arXiv:2603.29332 (cs) [Submitted on 31 Mar 2026] Title:Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity Authors:Yunyue Wei, Chenhui Zuo, Shanning Zhuang, Haixin Gong, Yaming Liu, Yanan Sui View a PDF of the paper titled Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity, by Yunyue Wei and 5 other authors View PDF HTML (experimental) 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 performance due to the curse of dimensionality in both control and reward design. Here we introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction. By integrating large-scale parallel GPU simulation with adversarial reward aggregation and value-guided flow exploration, the MS-Emulator framework overcomes key optimization bottlenecks in high-dimensional reinforcement learning for musculoskeletal control, which accurately reproduces a broad repertoire of motions in a whole-b...