Grasp EveryThing (GET)：面向鲁棒抓取的带触觉感知的1自由度三指夹爪
Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping
AI总结：
本研究提出一种1自由度三指V形构型的GET夹爪，采用参数化设计可适配多类机器人，集成外置摄像头触觉感知与力估计神经网络，实验证实其抓取表现优于标准平指，设计已开源。
摘要：
我们推出了Grasp EveryThing（GET）夹爪，这是一种新颖的1自由度三指设计，可稳固抓取多种形状和尺寸的物体。该夹爪安装在标准平行爪驱动器上，采用三根窄型锥形手指以二对一的构型排列，其中两根手指汇聚成V形。与传统的两平指设计相比，GET夹爪更能贴合物体几何形状并形成稳固抓取。受自相似性原理启发，这些V形手指可在广泛的物体尺寸范围内实现稳固抓取。为此，手指采用参数化设计，便于在配备平行爪夹爪的不同机器人本体上调整尺寸和互换使用。此外，我们还加入了刚性指甲结构，便于操控小型物体。触觉感知可通过外置摄像头集成到独立手指中。我们训练了一个神经网络，用于从触觉图像中估计法向力，在多种几何形状的物体上，平均验证误差为1.3牛。在遥操作下抓取15个物体并执行3项任务的实验中，GET手指的表现始终优于标准平指。所有兼容多种机器人本体（含触觉感知和不含触觉感知版本）的手指设计均已在GitHub上开源。
英文摘要：
We introduce the Grasp EveryThing (GET) gripper, a novel 1-DoF, 3-finger design for securely grasping objects of many shapes and sizes. Mounted on a standard parallel jaw actuator, the design features three narrow, tapered fingers arranged in a two-against-one configuration, where the two fingers converge into a V-shape. The GET gripper is more capable of conforming to object geometries and forming secure grasps than traditional designs with two flat fingers. Inspired by the principle of self-similarity, these V-shaped fingers enable secure grasping across a wide range of object sizes. Further to this end, fingers are parametrically designed for convenient resizing and interchangeability across robotic embodiments with a parallel jaw gripper. Additionally, we incorporate a rigid fingernail for ease in manipulating small objects. Tactile sensing can be integrated into the standalone finger via an externally-mounted camera. A neural network was trained to estimate normal force from tactile images with an average validation error of 1.3 N across a diverse set of geometries. In grasping 15 objects and performing 3 tasks via teleoperation, the GET fingers consistently outperformed standard flat fingers. All finger designs, compatible with multiple robotic embodiments, both incorporating and lacking tactile sensing, are available on GitHub.
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。
