# Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping

arXiv: https://arxiv.org/abs/2505.09771 · HTML: https://mlsanity.com/paper/2505.09771/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2025.

## Built on by (most cited first, 4 of 4)

- [DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation](https://mlsanity.com/paper/2509.04441/) (2025)
- [VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulation](https://mlsanity.com/paper/2511.21557/) (2025)
- [MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping](https://mlsanity.com/paper/2602.06504/) (2026)
- [RAPID: Reconfigurable, Adaptive Platform for Iterative Design](https://mlsanity.com/paper/2602.06653/) (2026)

## Abstract (arXiv)

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.