# Grasping Using Tactile Sensing and Deep Calibration

## TL;DR

This paper presents a tactile sensing feedback approach combined with deep calibration to improve robot grasping, reducing reliance on visual perception and addressing sensor bias for more effective manipulation.

## Contribution

It introduces a novel tactile feedback framework with deep calibration to enhance robot grasping accuracy and robustness.

## Key findings

- Effective tactile feedback improves grasp success.
- Deep calibration reduces sensor bias impact.
- Framework demonstrated successfully on a real robot.

## Abstract

Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback approach to address robot grasping task using force-torque tactile sensing. While visual perception is an essential part for gross reaching, constant utilization of this sensing modality can negatively affect the grasping process with overwhelming computation. In such case, human being utilizes tactile sensing to interact with objects. Inspired by, the proposed approach is presented and evaluated on a real robot to demonstrate the effectiveness of the suggested framework. Moreover, we utilize a deep learning framework called Deep Calibration in order to eliminate the effect of bias in the collected data from the robot sensors.