Ruka-v2开源灵巧手升级腕部与手指外展，提升机器人操作灵活性。
Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
- 采用腱驱动设计，新增腕部独立运动与手指外展自由度。
- 用户实测任务完成时间减少51.3%，成功率提升21.2%。
- 适合机器人学习、远程操控及人机协作场景的高精度操作。
缺乏可及且灵巧的机器人硬件是实现类人操作能力的关键瓶颈。去年我们发布了Ruka，一款完全开源、腱驱动的仿人手，具备11个自由度（每指2个，拇指3个），成本低于1300美元。它是首批完全开源的仿人手之一，并引入了一种数据驱动的指节控制方法，能够捕捉腱动力学特性。然而，Ruka缺少两个对模拟人类行为至关重要的自由度：腕部运动和手指外展/内收。本文提出Ruka-v2：一款完全开源、腱驱动的仿人手，配备解耦的2-自由度平行腕部及手指外展/内收功能。平行腕部支持平滑独立的屈伸与尺偏/桡偏运动，适用于狭窄空间操作（如橱柜）。外展支持抓取细长物体、手内旋转及书法等动作。我们通过用户研究对比Ruka与Ruka-v2在遥操作系统上的表现，结果显示任务完成时间减少51.3%，成功率提升21.2%。进一步展示了其在机器人学习中的全范围应用：涵盖13项灵巧任务的单臂与双臂遥操作，以及3项自主策略学习任务。所有3D打印文件、组装说明、控制器软件及视频均公开于https://ruka-hand-v2.github.io/。
原文摘要 · Abstract (English)
Lack of accessible and dexterous robot hardware has been a significant bottleneck to achieving human-level dexterity in robots. Last year, we released Ruka, a fully open-sourced, tendon-driven humanoid hand with 11 degrees of freedom - 2 per finger and 3 at the thumb - buildable for under $1,300. It was one of the first fully open-sourced humanoid hands, and introduced a novel data-driven approach to finger control that captures tendon dynamics within the control system. Despite these contributions, Ruka lacked two degrees of freedom essential for closely imitating human behavior: wrist mobility and finger adduction/abduction. In this paper, we introduce Ruka-v2: a fully open-sourced, tendon-driven humanoid hand featuring a decoupled 2-DOF parallel wrist and abduction/adduction at the fingers. The parallel wrist adds smooth, independent flexion/extension and radial/ulnar deviation, enabling manipulation in confined environments such as cabinets. Abduction enables motions such as grasping thin objects, in-hand rotation, and calligraphy. We present the design of Ruka-v2 and evaluate it against Ruka through user studies on teleoperated tasks, finding a 51.3% reduction in completion time and a 21.2% increase in success rate. We further demonstrate its full range of applications for robot learning: bimanual and single-arm teleoperation across 13 dexterous tasks, and autonomous policy learning on 3 tasks. All 3D print files, assembly instructions, controller software, and videos are available at https://ruka-hand-v2.github.io/ .
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