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

---

---

Abstract

This letter 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. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY.

Links

• doi:10.1109/LRA.2021.3065186

BibTeX (Download)

@article{Yang2021LearningBased,
title = {Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping},
author = {Linhan Yang and Xudong Han and Weijie Guo and Fang Wan and Jia Pan and Chaoyang Song},
doi = {10.1109/LRA.2021.3065186},
year  = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {2},
issue = {April},
pages = {3817 - 3824},
address = {Xi’an, China},
abstract = {This letter 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. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY.},
keywords = {Corresponding Author, IEEE Robot. Autom. Lett. (RA-L), JCR Q2},
pubstate = {published},
tppubtype = {article}
}