Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping

TL;DR

This paper introduces a soft tactile finger with omni-directional adaptation using optical fibers and machine learning, enabling real-time force prediction and reconfigurable grasping for improved robustness in rigid-soft interactions.

Contribution

It presents a novel soft tactile finger design with optical fibers and a machine learning model for real-time force prediction, integrated into a reconfigurable gripper for adaptive grasping.

Findings

Successful real-time force, torque, and contact prediction.

Enhanced grasping robustness demonstrated through experiments.

Reconfigurable finger arrangement improves grasp adaptability.

Abstract

This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.