ISyHand：一款带有关节手掌的灵巧多指机器人手
ISyHand: A Dexterous Multi-finger Robot Hand with an Articulated Palm
AI总结：
本研究推出低成本开源多指机器人手ISyHand，采用关节手掌设计在小幅牺牲拟人化的前提下提升灵巧性，通过仿真与实体实验验证了其在手内操作任务中的优异表现。
摘要：
人形机器人与定制化制造解决方案的快速发展，已将灵巧操作推至现代机器人学的前沿领域。在过去十年间，多款价格高昂的灵巧手已推向市场，但近期硬件设计（尤其是伺服电机与3D打印技术）的进步，催生了大量成本更低的开源灵巧手。大多数灵巧手采用拟人化设计，以便使用标准人类工具，而提升灵巧性的尝试往往会牺牲拟人化程度。我们推出了开源的ISyHand（发音为easy-hand），这是一款高灵巧性、低成本、易于制造的关节伺服驱动机器人手。该手采用市售Dynamixel电机、紧固件与3D打印部件，可在4小时内完成组装，总材料成本约为1300美元。ISyHand独特的关节手掌设计，在仅小幅牺牲拟人化程度的情况下提升了整体灵巧性。为验证关节手掌的实用性，我们在仿真环境中使用强化学习训练该手完成经典的手内操作任务：立方体重定向。我们开展的新颖系统性实验表明，仿真版ISyHand在训练初期的表现优于两款最具可比性的灵巧手；在策略收敛后，三者表现相近；且ISyHand的表现显著优于其自身设计的固定手掌版本。此外，我们将经立方体重定向训练的策略部署到实体手上，验证了其执行现实世界灵巧操作的能力。
英文摘要：
The rapid increase in the development of humanoid robots and customized manufacturing solutions has brought dexterous manipulation to the forefront of modern robotics. Over the past decade, several expensive dexterous hands have come to market, but advances in hardware design, particularly in servo motors and 3D printing, have recently facilitated an explosion of cheaper open-source hands. Most hands are anthropomorphic to allow use of standard human tools, and attempts to increase dexterity often sacrifice anthropomorphism. We introduce the open-source ISyHand (pronounced easy-hand), a highly dexterous, low-cost, easy-to-manufacture, on-joint servo-driven robot hand. Our hand uses off-the-shelf Dynamixel motors, fasteners, and 3D-printed parts, can be assembled within four hours, and has a total material cost of about 1,300 USD. The ISyHands's unique articulated-palm design increases overall dexterity with only a modest sacrifice in anthropomorphism. To demonstrate the utility of the articulated palm, we use reinforcement learning in simulation to train the hand to perform a classical in-hand manipulation task: cube reorientation. Our novel, systematic experiments show that the simulated ISyHand outperforms the two most comparable hands in early training phases, that all three perform similarly well after policy convergence, and that the ISyHand significantly outperforms a fixed-palm version of its own design. Additionally, we deploy a policy trained on cube reorientation on the real hand, demonstrating its ability to perform real-world dexterous manipulation.
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