Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation
<|reference_start|>Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation: Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.<|reference_end|>
@article{rodriguez2024touch2touch:,
    title={Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation},
    author={Samanta Rodriguez, Yiming Dou, Miquel Oller, Andrew Owens and Nima
  Fazeli},
    journal={arXiv preprint arXiv:2409.08269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08269},
    primaryClass={cs.RO}
}