# Shape-independent Hardness Estimation Using Deep Learning and a GelSight Tactile Sensor

## TL;DR

This paper presents a deep learning-based method using GelSight tactile sensor data to estimate object hardness regardless of shape or contact conditions, enabling more versatile robotic tactile perception.

## Contribution

The work introduces a novel approach combining GelSight sensor data with neural networks for shape-independent hardness estimation under loose contact control.

## Key findings

- Accurately estimates hardness across various shapes and hardness levels.
- Uses deep neural networks to analyze high-resolution tactile images.
- Works with manual or robotic contact without precise control.

## Abstract

Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep constitutional (and recurrent) neural network. Experiments show that the neural net model can estimate the hardness of objects with different shapes and hardness ranging from 8 to 87 in Shore 00 scale.