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arXiv 最近完成扫描:等待完成 · 全站最近入库:2026-11-02 00:39各信源进度 →

统计口径与数据新鲜度

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新论文 · v1arXiv:2610.40285

MUSBRIC:面向可重构肌肉骨骼机器人的乐高兼容气动肌肉积木

MUSBRIC: LEGO-Compatible Pneumatic Muscle Bricks for Reconfigurable Musculoskeletal Robots

Qiyao Wang, Yinglei Zhu, Liang Zhou, Wenzhe Gong, Xin-Jun Liu, Huichan Zhao

AI 速览:作者提出一种乐高兼容的气动人工肌肉模块及配套 MuJoCo 仿真器,用于快速搭建和探索可重构肌肉骨骼机器人。

Musculoskeletal robots combine compliant actuators with articulated skeletons, but exploring alternative configurations often requires repeated fabrication. We present MUSBRIC, a modular hardware platform and accompanying MuJoCo-based simulator for reconfigurable musculoskeletal robots. LEGO-compatible endcaps turn fiber-reinforced extensile pneumatic artificial muscles into reusable, plug-and-play muscle bricks that connect directly to standard LEGO components. Experimentally identified actuator behavior supports corresponding musculoskeletal simulations. An elbow-joint prototype validates load-dependent flexion and extension, showing that pulley-guided routing enables reversible motion and a mechanical stop stabilizes the straight posture. A robotic hand demonstrates compliant grasping and multi-joint construction. Matched hardware and simulation sweeps across 15 leg configurations demonstrate systematic exploration of attachment geometry, while a quadruped assembled from four legs demonstrates walking. Together, these demonstrations span mechanism analysis, configuration exploration, and functional robot construction within a single reconfigurable system. The hardware designs and simulation code will be released as open source soon to facilitate reproduction and further development.

cs.RO
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36535

A generalist policy for muscle-driven control across individual musculoskeletal models

Bozhao Wang, Yinglei Zhu, Zhengqing Li, Zhiyuan Zhang, Weiming Kang, Yiheng He, XinJun Liu, Senhao Hou 等 10 位作者

Differences in musculoskeletal morphology challenge policy reuse across individual models. Here we present MyoAcross, a framework that combines individual musculoskeletal model construction, morphology-conditioned policy learning and virtual model augmentation to enable muscle-driven reference-motion tracking without individual fine-tuning. Across 31 held-out individual models, MyoAcross achieves 99.24% completion and reduces tracking error by 22.60% relative to an unconditioned multilayer perceptron. Policy reuse extends to models of 22 held-out individuals constructed from single-image shape estimates, measured height and body mass, and, with asymmetry augmentation, to models with unilateral lower-limb shortening. Comparisons with measurements from 120 additional individuals show agreement in vertical ground reaction force waveforms and channel-dependent agreement between simulated muscle activations and electromyography. These results establish a common control framework for comparative simulations of movement across individual musculoskeletal models.

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