Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies

• URL: http://arxiv.org/abs/2511.16596v1
• Date: Thu, 20 Nov 2025 17:49:08 GMT
• Title: Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
• Authors: Zohar Rimon, Elisei Shafer, Tal Tepper, Efrat Shimron, Aviv Tamar,
• Abstract summary: Palpation, the use of touch in medical examination, is almost exclusively performed by humans.<n>We investigate a proof of concept for an artificial palpation method based on self-supervised learning.
• Score: 16.362724941286697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a $\textit{representation}$ from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces -- the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.

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