The paper compares two leading motion‑imitation reinforcement learning pipelines—HyFyDy, which uses detailed musculotendon modeling, and MuJoCo, which focuses on computational speed. Using the same human motion‑capture and EMG data, both pipelines reproduce kinematics similarly, but HyFyDy’s muscle activation predictions align more closely with experimental EMG (RMSE 0.164, r = 0.4) than MuJoCo’s (RMSE 0.344, r = 0.11). The authors conclude that HyFyDy’s higher physiological realism makes it currently more suitable for musculoskeletal modeling, though both systems need further development for GPU‑parallelizable environments and robotic assistive‑device design.
By Ayah G. Ahmad, Claire E. Borden, Maegan Tucker
MyoMechanix is a multimodal dataset and framework for action quality assessment that incorporates muscle activity and other physiological signals alongside visual data. It contains over 7,500 samples of 20 weight‑loaded actions from 38 subjects, with synchronized RGB video, 3D pose, sEMG, and additional signals. The accompanying Fitness Knowledge Graph structures expert annotations into relationships among actions, phases, key steps, errors, and corrective feedback, enabling compositional scoring and interpretable assessment through the CUBIST engine. The project also introduces MyoMechanix‑AQA, MyoMechanix‑VideoQA, and a novel MyoMechanix‑Video2EMG task, demonstrating that multimodal sensing and structured representations improve performance, interpretability, and error attribution.
By Hao Yin, Paritosh Parmar, Lijun Gu, Lin Xu, Tianxiao Guo, Xiujin Liu, Tianyou Zheng, Yang Zhang, Weiwei Fu
arXiv:2609.37089v1 Announce Type: new
Abstract: Real-world videos provide rich demonstrations of manipulation, but turning them into reusable robot skills requires visually aligned environments, exec...
By Kerui Ren, Yingxiang Xu, Kaiwen Song, Linning Xu, Bo Dai, Mulin Yu, Tao Lu
The paper introduces the λ-hold controller, a minimal-task-reward approach inspired by the equilibrium-point hypothesis, to train a muscle-actuated skeletal model for human-like sprinting. By fixing each muscle’s EP threshold length λ over gait-phase intervals, the controller dramatically reduces the action space and the frequency of policy queries, enabling efficient exploration and learning within an hour of training. This method demonstrates that physiologically grounded control can produce realistic human motion in predictive musculoskeletal simulations.
By Jun Hyuk Lee, Chihyeong Lee, Jooeun Ahn
arXiv:2506. 08795v2 Announce Type: replace-cross Abstract: Limb loss affects millions globally, impairing physical function and reducing quality of life.
By Kaijie Shi, Wanglong Lu, Hanli Zhao, Vinicius Prado da Fonseca, Ting Zou, Xianta Jiang
arXiv:2508. 18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge.
By Boshi An, Alberto Silvio Chiappa, Merkourios Simos, Chengkun Li, Alexander Mathis