Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning

TL;DR

This paper introduces AFOP-ML, a meta-learning framework with automatic feature optimization for tactile recognition of shapes and materials, achieving high accuracy with limited training data in contact-rich robotic scenarios.

Contribution

It proposes an automatic feature optimization-enabled meta-learning network that adapts quickly to new classes and automatically determines optimal feature spaces for tactile recognition.

Findings

Achieves 96.08% accuracy in 5-way-1-shot classification.

Maintains 88.7% accuracy in 36-way-1-shot classification.

Demonstrates strong generalization to unseen shapes, materials, and perturbations.

Abstract

Tactile perception is indispensable for robots to implement various manipulations dexterously, especially in contact-rich scenarios. However, alongside the development of deep learning techniques, it meanwhile suffers from training data scarcity and a time-consuming learning process in practical applications since the collection of a large amount of tactile data is costly and sometimes even impossible. Hence, we propose an automatic feature optimization-enabled prototypical network to realize meta-learning, i.e., AFOP-ML framework. As a ``learn to learn" network, it not only adapts to new unseen classes rapidly with few-shot, but also learns how to determine the optimal feature space automatically. Based on the four-channel signals acquired from a tactile finger, both shapes and materials are recognized. On a 36-category benchmark, it outperforms several existing approaches by attaining an accuracy of 96.08% in 5-way-1-shot scenario, where only 1 example is available for training. It still remains 88.7% in the extreme 36-way-1-shot case. The generalization ability is further validated through three groups of experiment involving unseen shapes, materials and force/speed perturbations. More insights are additionally provided by this work for the interpretation of recognition tasks and improved design of tactile sensors.