# Controlled Tactile Exploration and Haptic Object Recognition

## TL;DR

This paper introduces a new in-hand object recognition method combining grasp stabilization and exploratory behaviors to improve shape and softness recognition, demonstrating significant performance gains over non-stabilized approaches.

## Contribution

The paper presents a novel approach integrating grasp stabilization with exploratory behaviors for improved tactile object recognition, validated through experimental comparison.

## Key findings

- Successfully distinguished 30 objects using the proposed method.
- Outperformed a benchmark method without grasp stabilization.
- Statistically significant improvements in recognition accuracy.

## Abstract

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects.We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method.