Skip to content

Latest commit

 

History

History
36 lines (30 loc) · 1.77 KB

README.md

File metadata and controls

36 lines (30 loc) · 1.77 KB
Raw

TactileSGNet

This repository contains code for the IROS 2020 paper "TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition". In this paper, we propose a novel spiking graph neural network for event-based tactile object recognition. To make use of local connectivity of taxels, we present several methods for organizing the tactile data in a graph structure. Based on the constructed graphs, we develop a spiking graph convolutional network. The event-driven nature of spiking neural network makes it arguably more suitable for processing the event-based data. Experimental results on two tactile datasets show that the proposed method outperforms other state-of-the-art spiking methods, achieving high accuracies of approximately 90% when classifying a variety of different household objects.

Architecture of TactileSGNet

Dependencies

  • Pytorch (tested on v1.4.0)
  • torchvision
  • Numpy
  • torch_geometric
  • tqdm
  • scikit-learn
  • CUDA 10
  • time

Usage

  • Unzip the Ev-Objects dataset, and put it under the same folder as these python files
  • Run the 'main.py' file to see the result

Questions?

For any questions regarding the code or the paper, please email me at gufq87 at gmail.com.

Citing

You may want to cite the paper

@inproceedings{gu2020tactilesgnet,
  title={TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition},
  author={Gu, Fuqiang and Sng, Weicong and Taunyazov, Tasbolat and Soh, Harold},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020}
}

Acknowledgement

Part of the code in this repository has been adapted from the following repo: https://github.com/yjwu17/BP-for-SpikingNN