Fuzzy-Depth Objects Grasping Based on FSG Algorithm and a Soft Robotic Hand

TL;DR

This paper introduces a novel approach combining a soft robotic hand and the FSG algorithm to improve autonomous grasping of fuzzy-depth objects, such as transparent or small items, with high success rates.

Contribution

It presents a new framework integrating a soft robotic hand with the FSG algorithm that uses RGB and depth images without scene reconstruction, enhancing grasping robustness for fuzzy-depth objects.

Findings

Achieved 84% success rate in grasping unseen fuzzy-depth objects.

Outperformed baseline methods in experimental tests.

Demonstrated robustness with a versatile soft robotic hand.

Abstract

Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective, and rapidly deployable autonomous grasping approach is still limited by those target objects with fuzzy-depth information. Examples are transparent, specular, flat, and small objects whose depth is difficult to be accurately sensed. In this work, we present a solution to those fuzzy-depth objects. The framework of our approach includes two major components: one is a soft robotic hand and the other one is a Fuzzy-depth Soft Grasping (FSG) algorithm. The soft hand is replaceable for most existing soft hands/grippers with body compliance. FSG algorithm exploits both RGB and depth images to predict grasps while not trying to reconstruct the whole scene. Two grasping primitives are designed to further increase robustness. The proposed method outperforms reference baselines in unseen fuzzy-depth objects grasping experiments (84% success rate).