Object recognition for robotics from tactile time series data utilising different neural network architectures

TL;DR

This paper explores neural network architectures like CNN and LSTM for classifying objects using tactile data from robots, comparing sensors and proposing data augmentation to improve accuracy.

Contribution

It introduces a method to enhance tactile object classification accuracy and compares CNN and LSTM architectures across different tactile sensors in a realistic setting.

Findings

Maximum accuracy improved to about 94% for both sensors.

Comparison of CNN and LSTM architectures on tactile data.

Proposed data augmentation method increased training data.

Abstract

Robots need to exploit high-quality information on grasped objects to interact with the physical environment. Haptic data can therefore be used for supplementing the visual modality. This paper investigates the use of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) neural network architectures for object classification on Spatio-temporal tactile grasping data. Furthermore, we compared these methods using data from two different fingertip sensors (namely the BioTac SP and WTS-FT) in the same physical setup, allowing for a realistic comparison across methods and sensors for the same tactile object classification dataset. Additionally, we propose a way to create more training examples from the recorded data. The results show that the proposed method improves the maximum accuracy from 82.4% (BioTac SP fingertips) and 90.7% (WTS-FT fingertips) with complete time-series data to about 94% for both sensor types.