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

arXiv: https://arxiv.org/abs/2101.12379 · HTML: https://mlsanity.com/paper/2101.12379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Proprioceptive Learning with Soft Polyhedral Networks](https://mlsanity.com/paper/2308.08538/) (2023)

## Abstract (arXiv)

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.