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Authors: Alessandro Pozzi 1 ; Luca Puricelli 1 ; Vincenzo Petrone 2 ; Enrico Ferrentino 2 ; Pasquale Chiacchio 2 ; Francesco Braghin 1 and Loris Roveda 3

Affiliations: 1 Department of Mechanical Engineering, Politecnico di Milano, 20133 Milano, Italy ; 2 Department of Computer Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy ; 3 Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), 6962 Lugano, Switzerland

Keyword(s): Physical Robot-Environment Interaction, Artificial Neural Networks, Optimized Interaction Control, Impedance Control.

Abstract: In industrial settings, robots are typically employed to accurately track a reference force to exert on the surrounding environment to complete interaction tasks. Interaction controllers are typically used to achieve this goal. Still, they either require manual tuning, which demands a significant amount of time, or exact modeling of the environment the robot will interact with, thus possibly failing during the actual application. A significant advancement in this area would be a high-performance force controller that does not need operator calibration and is quick to be deployed in any scenario. With this aim, this paper proposes an Actor-Critic Model Predictive Force Controller (ACMPFC), which outputs the optimal setpoint to follow in order to guarantee force tracking, computed by continuously trained neural networks. This strategy is an extension of a reinforcement learning-based one, born in the context of human-robot collaboration, suitably adapted to robot-environment interactio n. We validate the ACMPFC in a real-case scenario featuring a Franka Emika Panda robot. Compared with a base force controller and a learning-based approach, the proposed controller yields a reduction of the force tracking MSE, attaining fast convergence: with respect to the base force controller, ACMPFC reduces the MSE by a factor of 4.35. (More)

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Paper citation in several formats:
Pozzi, A.; Puricelli, L.; Petrone, V.; Ferrentino, E.; Chiacchio, P.; Braghin, F. and Roveda, L. (2023). Experimental Validation of an Actor-Critic Model Predictive Force Controller for Robot-Environment Interaction Tasks. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5; ISSN 2184-2809, SciTePress, pages 394-404. DOI: 10.5220/0012160700003543

@conference{icinco23,
author={Alessandro Pozzi. and Luca Puricelli. and Vincenzo Petrone. and Enrico Ferrentino. and Pasquale Chiacchio. and Francesco Braghin. and Loris Roveda.},
title={Experimental Validation of an Actor-Critic Model Predictive Force Controller for Robot-Environment Interaction Tasks},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={394-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012160700003543},
isbn={978-989-758-670-5},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Experimental Validation of an Actor-Critic Model Predictive Force Controller for Robot-Environment Interaction Tasks
SN - 978-989-758-670-5
IS - 2184-2809
AU - Pozzi, A.
AU - Puricelli, L.
AU - Petrone, V.
AU - Ferrentino, E.
AU - Chiacchio, P.
AU - Braghin, F.
AU - Roveda, L.
PY - 2023
SP - 394
EP - 404
DO - 10.5220/0012160700003543
PB - SciTePress