Tactile Perception through Fluid-Solid Interaction
Abstract
Soft tactile sensors elevate robotic touch through enhanced flexibility and adaptability, yet most existing designs depend on embedded electronics that are susceptible to interference and environmental limitations. In this work, we leverage fluid-solid interactions to develop a class of soft tactile sensors that operate entirely without electronics at the sensing site. The sensor comprises a fluid-filled elastomeric channel connected to only two external pressure sensors. Touching different regions of the elastomeric surface displaces the viscous fluid, producing distinct pressure patterns that encode both touch position and force. These signals are decoded through a machine learning framework that integrates feature extraction, soft clustering, and adaptive neuro-fuzzy inference to achieve accurate localization and force estimation. We validate this concept through single-point touch localization and force estimation in a linear (1D) sensor and extend the same sensing principle to 2D tactile mapping by routing the channel across the surface using space-filling curves, while maintaining the same minimal hardware setup. This simple approach remains effective in environments where conventional electronic sensors often fail, such as underwater or in the presence of magnetic interference.
Versions
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BibTeX
@misc{goshtasbi2026tactile,
title = {{Tactile Perception through Fluid-Solid Interaction}},
author = {Arman Goshtasbi and Minke Berghuis and Aida Parvaresh and Saravana Prashanth Murali Babu and Robert W. Style and Ahmad Rafsanjani},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03773},
eprint = {2610.03773},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03773},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Tactile Perception through Fluid-Solid Interaction
AU - Arman Goshtasbi
AU - Minke Berghuis
AU - Aida Parvaresh
AU - Saravana Prashanth Murali Babu
AU - Robert W. Style
AU - Ahmad Rafsanjani
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.03773
UR - https://arxiv.org/abs/2610.03773
AB - Soft tactile sensors elevate robotic touch through enhanced flexibility and adaptability, yet most existing designs depend on embedded electronics that are susceptible to interference and environmental limitations. In this work, we leverage fluid-solid interactions to develop a class of soft tactile sensors that operate entirely without electronics at the sensing site. The sensor comprises a fluid-filled elastomeric channel connected to only two external pressure sensors. Touching different regions of the elastomeric surface displaces the viscous fluid, producing distinct pressure patterns that encode both touch position and force. These signals are decoded through a machine learning framework that integrates feature extraction, soft clustering, and adaptive neuro-fuzzy inference to achieve accurate localization and force estimation. We validate this concept through single-point touch localization and force estimation in a linear (1D) sensor and extend the same sensing principle to 2D tactile mapping by routing the channel across the surface using space-filling curves, while maintaining the same minimal hardware setup. This simple approach remains effective in environments where conventional electronic sensors often fail, such as underwater or in the presence of magnetic interference.
ER -