AI 中文总结
MuJoCable为MuJoCo增加降阶缆传动模型，联合优化布线路径并映射张力，在滑轮基准误差低于0.5%，并揭示SpiRobs摩擦载荷增长，缩小模拟到现实的差距。
AI 中文摘要
腱传动降低了远端惯性并增加了柔顺性，然而布线、松弛和摩擦控制着运动和力的传递。诸如MuJoCo之类的主流刚体机器人模拟器不能联合解析移动的非圆形接触、单侧张力和分段摩擦。我们提出了MuJoCable，它为MuJoCo增加了一个降阶的、与构型相关的缆传动。其布线算法联合优化了跨越移动解析曲面和网格曲面的有序路径。单侧轴向定律、定向Capstan传播和节点虚功将该路径映射为分段张力和物体力。热启动引擎插件在模拟过程中施加这些力，并暴露路由和载荷状态以供设计。滑轮基准以低于0.5%的Capstan比率误差恢复了解析传动关系。在欠驱动的18关节SpiRobs上，MuJoCable揭示了摩擦驱动的载荷增长和关节旋转的近端重新分布，而原生腱无法表示这些。在SpiRobs和腱路由耦合手指上的硬件测试复现了观察到的运动序列。通过使物理穿线可执行，MuJoCable将模拟到现实差距的传动来源带入了制造前的路由、缆绳和执行器设计。
英文摘要
Tendon transmissions reduce distal inertia and add compliance, yet routing, slack, and friction govern motion and force transfer. Mainstream rigid-body robotics simulators such as MuJoCo do not jointly resolve moving noncircular contact, unilateral tension, and segment friction. We present MuJoCable, which adds a reduced-order, configuration-dependent cable transmission to MuJoCo. Its routing algorithm jointly optimizes an ordered path across moving analytic and mesh surfaces. A unilateral axial law, directional Capstan propagation, and nodal virtual work map this path to segment tensions and body forces. The warm-started engine plugin applies these forces during simulation and exposes route and load states for design. Pulley benchmarks recover analytical transmission relations with a Capstan-ratio error below 0.5%. On the underactuated 18-joint SpiRobs, MuJoCable reveals friction-driven load growth and proximal redistribution of joint rotation that the native tendon does not represent. Hardware tests on SpiRobs and a tendon-route-coupled finger reproduce observed motion sequences. By making physical threading executable, MuJoCable brings transmission sources of the simulation-to-reality gap into route, cable, and actuator design before fabrication.
Comments14 pages, 6 figures, 3 tables
