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Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.

Peng Zhang¹, Guoqi Yu², Dongri Shan²

¹School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

This study introduces a new dataset and a multi-scale convolutional neural network for identifying the strength levels of object tactile attributes like elasticity and hardness, improving recognition accuracy and stability.

Keywords:

attribute strength level convolution neural network robot operating system robot tactile

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Area of Science:

Materials Science
Computer Science
Robotics

Background:

Current research on tactile attributes often simplifies to binary states (e.g., hard/soft).
There is a lack of datasets and models capable of discerning nuanced strength levels (e.g., varying degrees of hardness or elasticity).

Purpose of the Study:

To address the gap in identifying the strength levels of tactile attributes.
To develop a robust method for classifying nuanced tactile properties beyond binary distinctions.

Main Methods:

Establishment of a novel tactile dataset focusing on the strength levels of elasticity and hardness.
Proposal and implementation of a multi-scale convolutional neural network (CNN) architecture.
Fusion of single-channel and multi-channel features within the CNN for enhanced attribute recognition.

Main Results:

The proposed multi-scale CNN demonstrated superior accuracy compared to other models in classifying tactile strength levels.
The network achieved improved precision and recall, indicating better identification of positive examples.
The model exhibited higher F1-scores across all classes and more stable recognition of each strength level.

Conclusions:

The developed multi-scale CNN effectively identifies nuanced strength levels of tactile attributes.
The new dataset and model advance the field of tactile sensing and material characterization.
This approach offers a more stable and accurate method for tactile attribute recognition.