用学习方法重设计低成本仿人机械手，实现灵活抓握与强承重。
RUKA: Rethinking the Design of Humanoid Hands with Learning
- 基于动作捕捉数据学习控制模型，优化腱驱动结构。
- 15个被动自由度，可完成多种类人抓握，抓力强且耐用。
- 3D打印+现成零件，开源设计，适合科研与教学使用。
灵巧操作是机器人系统的基础能力，但硬件在精度、紧凑性、强度和成本间存在权衡。现有控制方法限制了手部设计与应用。本文提出RUKA，一种腱驱动的仿人机械手，采用3D打印部件与现成组件，5指共15个被动自由度，支持多样类人抓握。其腱驱动结构在紧凑的人体尺寸下实现强力抓握。通过采集MANUS手套的动作捕捉数据，学习关节-执行器及指尖-执行器映射模型，提升控制精度。大量测试表明，RUKA在可达性、耐久性和抓取强度上优于其他机器人手。远程操控任务进一步验证其灵巧性。RUKA的开源设计、装配说明、代码与数据已公开于https://ruka-hand.github.io/。
原文摘要 · Abstract (English)
Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordability. Existing control methods impose compromises on hand designs and applications. However, learning-based approaches present opportunities to rethink these trade-offs, particularly to address challenges with tendon-driven actuation and low-cost materials. This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers with 15 underactuated degrees of freedom enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form factor. To address control challenges, we learn joint-to-actuator and fingertip-to-actuator models from motion-capture data collected by the MANUS glove, leveraging the hand's morphological accuracy. Extensive evaluations demonstrate RUKA's superior reachability, durability, and strength compared to other robotic hands. Teleoperation tasks further showcase RUKA's dexterous movements. The open-source design and assembly instructions of RUKA, code, and data are available at https://ruka-hand.github.io/.
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