Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation

Carolina Higuera; Akash Sharma; Taosha Fan; Chaithanya Krishna Bodduluri; Byron Boots; Michael Kaess; Mike Lambeta; Tingfan Wu; Zixi Liu; Francois Robert Hogan; Mustafa Mukadam

arXiv · 2506.14754

Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation

Carolina Higuera·Akash Sharma·Taosha Fan·Chaithanya Krishna Bodduluri·Byron Boots·Michael Kaess·Mike Lambeta·Tingfan Wu·Zixi Liu·Francois Robert Hogan·Mustafa Mukadam

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Abstract

We present Sparsh-X, the first multisensory touch representations across four tactile modalities: image, audio, motion, and pressure. Trained on ~1M contact-rich interactions collected with the Digit 360 sensor, Sparsh-X captures complementary touch signals at diverse temporal and spatial scales. By leveraging self-supervised learning, Sparsh-X fuses these modalities into a unified representation that captures physical properties useful for robot manipulation tasks. We study how to effectively integrate real-world touch representations for both imitation learning and tactile adaptation of sim-trained policies, showing that Sparsh-X boosts policy success rates by 63% over an end-to-end model using tactile images and improves robustness by 90% in recovering object states from touch. Finally, we benchmark Sparsh-X ability to make inferences about physical properties, such as object-action identification, material-quantity estimation, and force estimation. Sparsh-X improves accuracy in characterizing physical properties by 48% compared to end-to-end approaches, demonstrating the advantages of multisensory pretraining for capturing features essential for dexterous manipulation.

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Carolina Higuera, Akash Sharma, Taosha Fan, Chaithanya Krishna Bodduluri, Byron Boots, Michael Kaess, Mike Lambeta, Tingfan Wu, Zixi Liu, Francois Robert Hogan, Mustafa Mukadam. 2025-06-17. Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation. https://arxiv.org/abs/2506.14754

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