# CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors

arXiv: https://arxiv.org/abs/2305.12417 · HTML: https://mlsanity.com/paper/2305.12417/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 60 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Quantifying Translation-Invariance in Convolutional Neural Networks](https://mlsanity.com/paper/1801.01450/) (2017)
- [Robotic Tactile Perception of Object Properties: A Review](https://mlsanity.com/paper/1711.03810/) (2017)
- [Deep Learning for Tactile Understanding From Visual and Haptic Data](https://mlsanity.com/paper/1511.06065/) (2015)
- [A Flexible and Robust Large Scale Capacitive Tactile System for Robots](https://mlsanity.com/paper/1411.6837/) (2014)
- [Deep Learning for Surface Material Classification Using Haptic And Visual Information](https://mlsanity.com/paper/1512.06658/) (2015)
- [Iterative Closest Labeled Point for Tactile Object Shape Recognition](https://mlsanity.com/paper/1708.04436/) (2017)

## Built on by (most cited first, 3 of 3)

- [Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information](https://mlsanity.com/paper/2306.06423/) (2023)
- [TiltXter: CNN-based Electro-tactile Rendering of Tilt Angle for Telemanipulation of Pasteur Pipettes](https://mlsanity.com/paper/2409.15838/) (2024)
- [When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2603.25115/) (2026)

## Abstract (arXiv)

Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN (TactNet) trained from scratch with tactile information. The transfer learning approach can be carried out by retraining the classification layers of the network or replacing these layers with an SVM. Overall, 11 configurations based on these methods have been tested: 8 transfer learning-based, and 3 TactNet-based. Moreover, a study of the performance of the methods and a comparative discussion with the current state-of-the-art on tactile object recognition is presented.