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Model predictive control for dynamic cloth manipulation: parameter learning and experimental validation Luque Acera, Adrià Parent Alonso, David Colomé Figueras, Adrià Ocampo-Martínez, Carlos Torras, Carme Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. ROBiri - Grup de Percepció i Manipulació Robotitzada de l'IRI Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control Àrees temàtiques de la UPC::Informàtica::Robòtica Cloth manipulation Model predictive control Reinforcement learning Robot perception Classificació INSPEC::Control theory::Predictive control © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Robotic cloth manipulation is a challenging problem for robotic systems. Textile items can adopt multiple configurations and shapes during their manipulation. Hence, robots should not only understand the current configuration of the item but also be able to predict its future possible behaviours and perform real-time control during manipulation. This paper addresses the problem of indirectly controlling the configuration of certain points of a textile object, by applying actions on other parts of it through the use of a Model Predictive Control (MPC) strategy. MPC allows to foresee the behavior of indirectly controlled points, while satisfying physical/operational constraints. This is done by first identifying the optimal control signals that may constitute the desired future cloth configuration. After that, a dynamic model of the item will be used and sensor data will allow to update the belief on the object’s state and close the loop. This paper investigates how grasping the upper corners of a square piece of cloth can allow to track a reference trajectory of the pieces’ lower corners. To do so, we propose and validate a linear cloth model that allows solving the MPC optimization problem in real time. Reinforcement Learning (RL) techniques are used to learn the parameters of the proposed cloth model and to tune the resulting MPC. The full control scheme was implemented and executed in a real robot, obtaining accurate tracking results in adverse conditions. This work was developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”), which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programmee (Advanced Grant agreement No 741930). The work is also supported by Project PID2020- 118649RB-I00 funded by MCIN/ AEI /10.13039/501100011033. Peer Reviewed Postprint (author's final draft) 2024-07 Article Luque, A. [et al.]. Model predictive control for dynamic cloth manipulation: parameter learning and experimental validation. "IEEE transactions on control systems technology", Juliol 2024, vol. 32, núm. 4, p. 1254-1270. 1063-6536 https://hdl.handle.net/2117/424285 10.1109/TCST.2024.3362514 eng https://ieeexplore.ieee.org/document/10436531 info:eu-repo/grantAgreement/EC/H2020/741930/EU/CLOTH manIpulation Learning from DEmonstrations/CLOTHILDE Open Access Attribution-NonCommercial-NoDerivatives 4.0 International 17 p. application/pdf