This work proposes a novel generative design tool for passive grippers -- robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/

翻译：本文提出了一种针对被动夹爪的新型生成式设计工具——这种机器人末端执行器无需额外驱动，而是利用机器人手臂已有的自由度来完成抓取任务。被动夹爪因其在成本与性能之间提供的有趣折衷而被广泛使用，但现有设计能抓取的形状类型十分有限。本研究提出利用快速制造与设计优化来扩展被动抓取的形状空间。我们的新型生成式设计算法接收目标物体及其相对于机器人手臂的定位信息，生成一个可3D打印的被动夹爪，使其能够稳定拾取该物体。为实现这一目标，我们解决了同时优化夹爪形状与插入轨迹的关键挑战，以确保实现被动稳定的抓取。我们通过包含22个物体（共23组实验）的测试套件对方法进行评估，所有实验均通过实物测试来弥合虚拟与现实的差距。代码与数据见 https://homes.cs.washington.edu/~milink/passive-gripper/