# Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network

arXiv: https://arxiv.org/abs/2203.06183 · HTML: https://mlsanity.com/paper/2203.06183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds](https://mlsanity.com/paper/2003.09644/) (2020)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification](https://mlsanity.com/paper/1808.06698/) (2018)
- [FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network](https://mlsanity.com/paper/1810.02653/) (2018)
- [SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications](https://mlsanity.com/paper/2107.14598/) (2021)

## Abstract (arXiv)

For humans, our "senses of touch" have always been necessary for our ability to precisely and efficiently manipulate objects of all shapes in any environment, but until recently, not many works have been done to fully understand haptic feedback. This work proposed a novel method for getting a better shape descriptor than existing methods for classifying an object from multiple tactile data collected from a tactile glove. It focuses on improving previous works on object classification using tactile data. The major problem for object classification from multiple tactile data is to find a good way to aggregate features extracted from multiple tactile images. We propose a novel method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile features considering relations among different features by using Graph Convolutional Network. Our model outperforms previous methods on the STAG dataset with an accuracy of 81.82%.