Sparsh-skin is a transformer-based backbone for full hand tactile sensing with the
Xela sensor. This model is trained using self-distillation SSL and is specifically adapted for full hand Xela sensing, accounting for hand configuration, etc.
Disclaimer: This model card was written by the Sparsh-skin authors. The Transformer architetcure and DINO objectives have been adapted for full hand tactile SSL purposes.
You can utilize the Sparsh-skin model to extract touch representations for the Xela sensor. You have two options:
Both options enable you to take advantage of the powerful touch representations learned by the Sparsh-skin model.
For detailed instructions on how to load the encoder and integrate it into your downstream task, please refer to our
GitHub repository.
1@inproceedings{
2sharma2025selfsupervised,
3title={Self-supervised perception for tactile skin covered dexterous hands},
4author={Akash Sharma and Carolina Higuera and Chaithanya Krishna Bodduluri and Zixi Liu and Taosha Fan and Tess Hellebrekers and Mike Lambeta and Byron Boots and Michael Kaess and Tingfan Wu and Francois Robert Hogan and Mustafa Mukadam},
5booktitle={9th Annual Conference on Robot Learning},
6year={2025},
7url={https://openreview.net/forum?id=eLeCrM5PEO}
8}
