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

arXiv: https://arxiv.org/abs/2109.04573 · HTML: https://mlsanity.com/paper/2109.04573/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Learning for Tactile Understanding From Visual and Haptic Data](https://mlsanity.com/paper/1511.06065/) (2015)
- [Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?](https://mlsanity.com/paper/1809.05551/) (2018)

## Built on by (most cited first, 1 of 1)

- [TactoFind: A Tactile Only System for Object Retrieval](https://mlsanity.com/paper/2303.13482/) (2023)

## Abstract (arXiv)

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.