RUKA：通过学习重新思考仿人机械手的设计
RUKA: Rethinking the Design of Humanoid Hands with Learning
AI总结：
针对机器人灵巧操作受硬件权衡限制的问题，本研究提出腱驱动仿人机械手RUKA，基于学习方法优化控制，实现紧凑经济且类人抓取能力，开源设计可提升相关研究可及性。
摘要：
灵巧操作是机器人系统的基本能力，但受限于硬件在精度、紧凑性、强度和经济性之间的权衡，进展有限。现有控制方法对机械手设计和应用造成妥协。然而，基于学习的方法为重新思考这些权衡提供了机会，尤其是解决腱驱动和低成本材料的挑战。本研究提出RUKA，一种紧凑、经济且实用的腱驱动仿人机械手。由3D打印部件和现成组件制成，RUKA有5根手指，15个欠驱动自由度，可实现多样化类人抓取。其腱驱动结构使紧凑人型尺寸具备强大抓取力。为解决控制挑战，利用手的形态精度，从MANUS手套采集的动作捕捉数据中学习关节-执行器和指尖-执行器模型。广泛评估表明，RUKA在可达性、耐用性和强度上优于其他机械手。遥操作任务进一步展示其灵巧动作。RUKA的开源设计、组装说明、代码和数据可在https://ruka-hand.github.io/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
