Title: Visual-Haptic-Kinesthetic Object Recognition with Multimodal Transformer

• CRNN-SA
• CRNN-CA

• python >= 3.7
• scipy
• tensorflow-gpu >= 2.5.0
• Keras >= 2.3.1
• PIL
• pandas

1. Download and unzip the dataset from AU Dataset for Visuo-Haptic Object Recognition for Robots.
2. Run picenhance.py to enhance the visual data.
3. Run functions in Data_make.py to pre-process the data.
4. You can request the pre-processed data from the author of this article (GitHub:Jokerr-12).

--Notes for getting started--

There is no complicated tuning of parameters for this work, and you can probably adjust the parameters of the network to achieve better results.

• run CRNN_SA method

python runtrain.py --epochs 200 --batch_size 8 --model SA

• run CRNN_CA method

python runtrain.py --epochs 200 --batch_size 8 --model CA

• test CRNN_SA save_model

python runtest.py --model SA

• test CRNN_CA save_model

python runtest.py --model CA

@InProceedings{10.1007/978-3-031-44195-0_20,

author="Zhou, Xinyuan and Lan, Shiyong and Wang, Wenwu and Li, Xinyang and Zhou, Siyuan and Yang, Hongyu",

title="Visual-Haptic-Kinesthetic Object Recognition with Multimodal Transformer",

booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2023", year="2023",

publisher="Springer Nature Switzerland", address="Cham", pages="233--245", isbn="978-3-031-44195-0" }