Design of an Optoelectronically Innervated Gripper for Rigid-Soft Interactive Grasping

TL;DR

This paper introduces an adaptive soft robotic finger with optical sensing and machine learning to improve grasp stability and manipulation by actively conforming to object shapes.

Contribution

It presents a novel omni-directional soft finger with embedded optical fibers and a low-cost gripper design for enhanced adaptive grasping and tactile feedback.

Findings

Enhanced grasp stability through active adaptation

Successful implementation of optical fiber sensing for deformation detection

Improved manipulation capabilities using tactile information

Abstract

Over the past few decades, efforts have been made towards robust robotic grasping, and therefore dexterous manipulation. The soft gripper has shown their potential in robust grasping due to their inherent properties-low, control complexity, and high adaptability. However, the deformation of the soft gripper when interacting with objects bring inaccuracy of grasped objects, which causes instability for robust grasping and further manipulation. In this paper, we present an omni-directional adaptive soft finger that can sense deformation based on embedded optical fibers and the application of machine learning methods to interpret transmitted light intensities. Furthermore, to use tactile information provided by a soft finger, we design a low-cost and multi degrees of freedom gripper to conform to the shape of objects actively and optimize grasping policy, which is called Rigid-Soft Interactive Grasping. Two main advantages of this grasping policy are provided: one is that a more robust grasping could be achieved through an active adaptation; the other is that the tactile information collected could be helpful for further manipulation.