Designing Pin-pression Gripper and Learning its Dexterous Grasping with Online In-hand Adjustment

TL;DR

This paper presents a novel pin-pression gripper with independently adjustable pins inspired by toys, combined with reinforcement learning and curriculum schemes to achieve highly adaptable and robust grasping, including in-hand object re-orientation, with successful sim-to-real transfer.

Contribution

The paper introduces a new pin-pression gripper design with independent pin control and a reinforcement learning approach with curriculum learning for dexterous grasping.

Findings

Demonstrates highly flexible and robust grasping capabilities.

Achieves better generality to unseen objects.

Shows successful sim-to-real transfer on physical hardware.

Abstract

We introduce a novel design of parallel-jaw grippers drawing inspiration from pin-pression toys. The proposed pin-pression gripper features a distinctive mechanism in which each finger integrates a 2D array of pins capable of independent extension and retraction. This unique design allows the gripper to instantaneously customize its finger's shape to conform to the object being grasped by dynamically adjusting the extension/retraction of the pins. In addition, the gripper excels in in-hand re-orientation of objects for enhanced grasping stability again via dynamically adjusting the pins. To learn the dynamic grasping skills of pin-pression grippers, we devise a dedicated reinforcement learning algorithm with careful designs of state representation and reward shaping. To achieve a more efficient grasp-while-lift grasping mode, we propose a curriculum learning scheme. Extensive evaluations demonstrate that our design, together with the learned skills, leads to highly flexible and robust grasping with much stronger generality to unseen objects than alternatives. We also highlight encouraging physical results of sim-to-real transfer on a physically manufactured pin-pression gripper, demonstrating the practical significance of our novel gripper design and grasping skill. Demonstration videos for this paper are available at https://github.com/siggraph-pin-pression-gripper/pin-pression-gripper-video.