Optical tactile force sensing with a soft transparent sensory skin using laser time-of-flight measurement and nonlinear modeling

Zore, Aleš (author), Babić, Marina (author), Munih, Marko (author), Kamnik, Roman (author)

This work presents a machine learning-based framework for predicting applied contact force from the optical response of a time-of-flight (ToF) sensor embedded in a soft, optically transparent tactile sensing medium, SoftSkin. The SoftSkin medium acts as a light-guiding structure, enabling light propagation via total internal reflection. External pressure deforms the light-guide material, resulting in measurable changes in the optical response. A robotic test setup equipped with a force–torque sensor enabled controlled and repeatable experiments. Training datasets were collected at three indentation velocities and six applied force levels. Three multilayer perceptron (MLP) architectures were developed and evaluated. The single-input method used only photon counts, whereas the dual-input method incorporated temporal ambient-light conditions. A third approach, the derivative-enhanced dual-input method, additionally integrates temporal gradients of the photon response. For each MLP model, a Random Forest (RF) residual corrector was applied to reduce prediction errors. Performance was evaluated on an independent test set comprising unseen force magnitudes at three indentation velocities. All MLP–RF models achieved a mean absolute prediction error below 1 N, with the derivative-enhanced dual-input model demonstrating the best performance. The results confirm that ToF-based optical tactile sensing, combined with lightweight neural network architectures and residual learning, enables accurate reconstruction of dynamic force profiles despite sensor noise and the limited frame rate of the ToF sensor.

• Language: English
• Keywords: applied contact force prediction, LIDAR, light-guide, neural network, NN, soft sensor skin, tactile sensing, light detection and ranging, time of flight, ToF
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: Str. 25717-25729
• Numbering: Vol. 26, no. 17
• PID: 20.500.12556/RUL-187829
• UDC: 004.8:681.5
• ISSN on article: 1558-1748
• DOI: 10.1109/JSEN.2026.3713612
• COBISS.SI-ID: 288831747
• Publication date in RUL: 15.09.2026

Record is a part of a journal

• Title: IEEE sensors journal
• Shortened title: IEEE sens. j.
• Publisher: Institute of Electrical and Electronics Engineers
• ISSN: 1558-1748
• COBISS.SI-ID: 1542314

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: napovedovanje kontaktne sile, LIDAR, optični medij za prenos svetlobe, nevronska mreža, mehka senzorna površina, taktilno zaznavanje

Projects

• Funder: EC - European Commission
• Funding programme: HE
• Project number: 101070310
• Name: Physical Cognition for Intelligent Control and Safe Human-Robot Interaction
• Acronym: Sestosenso

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0228
• Name: Analiza in sinteza gibanja pri človeku in stroju