Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks

Kevin Saad, Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio, Francesco Braghin, Loris Roveda, Loris Roveda

2025

Abstract

As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either require extensive tuning, or demand expert knowledge of the environment, which is often impractical in real-world applications. This work proposes a novel control strategy leveraging Neural Networks (NNs) to enhance the force-tracking behavior of a Direct Force Controller (DFC). Unlike similar previous approaches, it accounts for the manipulator’s tangential velocity, a critical factor in force exertion, especially during fast motions. The method employs an ensemble of feedforward NNs to predict contact forces, then exploits the prediction to solve an optimization problem and generate an optimal residual action, which is added to the DFC output and applied to an impedance controller. The proposed Velocity-augmented Artificial intelligence Interaction Controller for Ambiguous Models (VAICAM) is validated in the Gazebo simulator on a Franka Emika Panda robot. Against a vast set of trajectories, VAICAM achieves superior performance compared to two baseline controllers.

Paper Citation

in Harvard Style

Saad K., Petrone V., Ferrentino E., Chiacchio P., Braghin F. and Roveda L. (2025). Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 394-401. DOI: 10.5220/0013830700003982

in Bibtex Style

@conference{icinco25,
author={Kevin Saad and Vincenzo Petrone and Enrico Ferrentino and Pasquale Chiacchio and Francesco Braghin and Loris Roveda},
title={Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={394-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013830700003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks
SN - 978-989-758-770-2
AU - Saad K.
AU - Petrone V.
AU - Ferrentino E.
AU - Chiacchio P.
AU - Braghin F.
AU - Roveda L.
PY - 2025
SP - 394
EP - 401
DO - 10.5220/0013830700003982
PB - SciTePress