[Submitted on 19 Oct 2026]
Title:Towards Interaction State Recognition in Sensorized Gecko-Inspired Adhesive Interfaces for Orbital Servicing
View a PDF of the paper titled Towards Interaction State Recognition in Sensorized Gecko-Inspired Adhesive Interfaces for Orbital Servicing, by Eduart Abdullahu and 2 other authors
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Abstract:Reliable contact establishment remains a critical challenge for autonomous on-orbit servicing (OOS), particularly when interacting with non-cooperative targets. Gecko-inspired dry adhesives provide a lightweight, passive, and residue-free solution for robotic capture in space, yet current systems operate in open loop, without the ability to estimate their own contact or adhesion state during interaction. This paper presents a real-time interaction-awareness framework that enables sensorized gecko-inspired adhesive interfaces to estimate their contact state throughout the adhesion lifecycle using embedded capacitive sensing. Five discrete interaction states (idle, proximity, contact, engaged, and disengaged) are classified using experimentally collected and augmented sensor data, enabling a comparative evaluation of six lightweight machine learning models suitable for embedded deployment. Among the evaluated models, Random Forest achieved the highest offline classification accuracy of 95.65%. For online deployment, combining Random Forest with a rule-based state machine improved accuracy from 78.02% to 93.14%, achieving a macro F1-score of 0.9343 while maintaining a 53 ms inference cycle. These results demonstrate that incorporating physics-consistent state constraints significantly improves robustness during realtime inference, representing a step toward interaction-aware capture in future autonomous orbital servicing missions.
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2610.27141 [cs.RO]
(or arXiv:2610.27141v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27141
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Eduart Abdullahu [view email] [v1] Mon, 19 Oct 2026 18:36:46 UTC (22,946 KB)
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