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.
翻译:人形机器人和定制化制造解决方案的快速发展,已将灵巧操作推向了现代机器人学的前沿。在过去十年中,市场上出现了几款昂贵的灵巧手,但硬件设计(尤其是伺服电机和3D打印技术)的进步,最近催生了一批更廉价的开源灵巧手。大多数灵巧手采用拟人化设计以便使用标准的人类工具,而提升灵巧性的尝试往往以牺牲拟人化为代价。我们推出了开源ISyHand(发音同easy-hand),这是一种高度灵巧、低成本、易于制造、采用关节伺服驱动的机器人手。我们的手采用现成的Dynamixel电机、紧固件和3D打印部件,可在四小时内完成组装,总材料成本约为1300美元。ISyHand独特的关节化掌部设计以仅适度牺牲拟人化为代价,显著提升了整体灵巧性。为展示关节化掌部的实用性,我们在仿真环境中使用强化学习训练该手执行一项经典的手内操作任务:立方体重定向。我们新颖、系统的实验表明,在训练早期阶段,仿真ISyHand的性能优于两款最具可比性的灵巧手;在策略收敛后,三者的表现相近;并且ISyHand显著优于其自身设计的固定掌部版本。此外,我们将立方体重定向任务上训练的策略部署到实体手上,证明了其执行现实世界灵巧操作的能力。