Towards Interaction State Recognition in Sensorized Gecko-Inspired Adhesive Interfaces for Orbital Servicing

Eduart Abdullahu; Francesco Grella; Carol Martinez

cs.ROOct 19, 2026

Towards Interaction State Recognition in Sensorized Gecko-Inspired Adhesive Interfaces for Orbital Servicing

Authors: Eduart Abdullahu, Francesco Grella, Carol Martinez

Organizations: Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg

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.

Figures & tables

Fig. 1 : Potential docking scenario. Sensorized gecko-inspired interfaces can provide interaction-state feedback to support autonomous decision-making in future OOS missions.

Fig. 2 : Interaction-state recognition pipeline. Raw steady-state measurements are augmented with synthetic transitions, noise, and sporadic spikes. Statistical features are extracted using an $N$ -step sliding window and classified by the Random Forest, followed by state-machine post-processing to obtain the final interaction-state estimate.

Feature	Description
Mean ( $\mu$ )	Average signal amplitude
Median	Robust central tendency estimate
Standard deviation ( $\sigma$ )	Signal variability
Variance ( $\sigma^{2}$ )	Dispersion magnitude
Minimum / Maximum	Signal extrema
Range	Peak-to-peak amplitude span

TABLE I : Extracted signal features for classification

State	Idle	Prox	Contact	Engaged	Disengaged
Idle	True	True	-	-	-
Prox	True	True	True	-	-
Contact	-	True	True	True	-
Engaged	-	-	-	True	True
Disengaged	-	True	-	-	True

TABLE II : Permitted interaction-state transitions enforced by the state machine. Rows indicate the current state and columns the admissible subsequent states.

Fig. 3 : Experimental setup. A custom sliding mechanism integrated into a 3D-printed fixture provides controlled engagement to improve motion reliability and disengagement of the sensorized gecko-inspired adhesive. The renders illustrate the five interaction states considered in this work.

Interaction State	Steady Data	Augmented Data
Idle	4,167	15,298
Proximity	7,948	13,428
Contact	3,976	6,903
Engaged	3,704	6,771
Disengaged	3,676	5,991
Total	23,471	48,391

TABLE III : Summary of the steady-state and augmented datasets used for classifier training and evaluation.

Classifier	Steady Data	Augmented Data
Gradient Boosting	0.9790	0.9439
Logistic Regression	0.7917	0.9168
K-Nearest Neighbors	0.9757	0.9153
Support Vector Machine	0.8417	0.9134
Random Forest	0.9764	0.9565
Extremely Randomized Trees	0.9816	0.9421

TABLE IV : Classification accuracy on steady-state and augmented datasets.

Hyperparameter	Value
Number of estimators ( $n_{\mathrm{estimators}}$ )	550
Maximum depth ( $max_{\mathrm{depth}}$ )	None
Minimum samples split ( $min_{\mathrm{samples\_split}}$ )	6
Minimum samples leaf ( $min_{\mathrm{samples\_leaf}}$ )	2
Maximum features ( $max_{\mathrm{features}}$ )	None
Bootstrap sampling ( $bootstrap$ )	True

TABLE V : Optimized Random Forest hyperparameters obtained through Optuna search

Fig. 4 : Confusion matrices on the augmented dataset. The largest confusion occurs between the Contact and Disengaged states due to their similar mechanical configurations.

Fig. 5 : Representative images of the online evaluation covering the five interaction states.

Fig. 6 : Comparison of interaction-state predictions during online operation, where colors represent the different interaction states. The top plot shows the measured capacitance signal, while the three remaining plots present the predicted interaction states obtained using (top) ET trained on the steady-state dataset, (middle) Random Forest trained on the augmented dataset, and (bottom) Random Forest combined with the proposed state-machine post-processing (RF+SM).

Model	Accuracy	Precision (W)	Recall (W)	F1 (W)	F1 (Macro)
ET	0.6313	0.5427	0.6313	0.5784	0.4598
RF	0.7802	0.7942	0.7802	0.7787	0.6417
RF+SM	0.9314	0.9364	0.9314	0.9307	0.9343

TABLE VI : Performance comparison of the evaluated models during online operation. (W) denotes weighted metrics.

Fig. 7 : Confusion matrices for the online evaluation.

Space Robotics

Space robotics develops robots and autonomy for exploring, assembling, and maintaining spacecraft and planetary environments.

See all 71 papers

Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts
Jul 21, 2026Ali Maghami, Merten Stender, Michele Ciavarella +1Neural Surrogate Modeling
ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation
Sep 18, 2026Dmitriy Kosenkov, Daniia Zinniatullina, Miguel Altamirano Cabrera +3Robotic Grasping Contact-Rich Robotic Manipulation
Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
Foundation Models for Autonomous Space Robotics: Architectures, Physical Adaptation, and Validation
Oct 16, 2026Zizhe Wang, Jia Deng, Xin Lu +4Space Robotics Robot Foundation Models
Astroup Lab, Beijing, China · Harbin Institute of Technology, Beijing Research Institute · Peking University

Jul 21, 2026J/K move · Enter open · S save

Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts

Ali Maghami, Merten Stender, Michele Ciavarella, Antonio Papangelo

Neural Surrogate Modeling

Open paper AskPDF arXiv

Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from

0.2

3.2

. To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of

5.0\times10^{-4}

, a median pull-off-force error of

\approx2.2\%

, and a median hysteresis error of

\approx1.1\%

. For the held-out protocols, the model predicts a complete force trajectory with a median inference time of

0.16

s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.

Abstract

Space Robotics

Explore similar work

Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts

ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation

Foundation Models for Autonomous Space Robotics: Architectures, Physical Adaptation, and Validation