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Design Example

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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...

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Touchformer: A Transformer-Based Two-Tower Architecture for Tactile Temporal Signal Classification.

Chongyu Liu, Hong Liu, Hu Chen

IEEE Transactions on Haptics

|December 25, 2023

Summary

This study introduces Touchformer, a Transformer-based model that enhances robot perception by improving haptic temporal signal recognition. The novel approach effectively classifies complex haptic data, even with limited samples.

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Area of Science:

Robotics and Artificial Intelligence
Machine Learning
Signal Processing

Background:

Haptic temporal signal recognition is crucial for robot perception.
Existing methods face challenges with diverse haptic datasets and small sample sizes.

Purpose of the Study:

To enhance classification performance for haptic temporal signal datasets.
To develop a robust model for robot perception using Transformer architecture.

Main Methods:

Proposed a Transformer-based two-tower model named Touchformer.
Extracted temporal and spatial features separately using self-attention mechanisms.
Employed data augmentation to address small sample dataset characteristics.

Main Results:

Touchformer significantly outperformed benchmark models on three public datasets.
The model demonstrated improved recognition performance and robustness.

Conclusions:

The proposed Touchformer model offers an effective solution for haptic temporal signal classification.
This advancement contributes to improved robot perception capabilities.