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Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases Laplaza Galindo, Javier Pumarola Peris, Albert Moreno-Noguer, Francesc Sanfeliu Cortés, Alberto Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI Àrees temàtiques de la UPC::Informàtica::Automàtica i control Automation Attention deep learning Human motion prediction Handover operation Classificació INSPEC::Automation © 2021 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. This work proposes a human motion prediction model for handover operations. We use in this work, the different phases of the handover operation to improve the human motion predictions. Our attention deep learning based model takes into account the position of the robot’s End Effector and the phase in the handover operation to predict future human poses. Our model outputs a distribution of possible positions rather than one deterministic position, a key feature in order to allow robots to collaborate with humans. The attention deep learning based model has been trained and evaluated with a dataset created using human volunteers and an anthropomorphic robot, simulating handover operations where the robot is the giver and the human the receiver. For each operation, the human skeleton is obtained with an Intel RealSense D435i camera attached inside the robot’s head. The results shown a great improvement of the human’s right hand prediction and 3D body compared with other methods. Work supported under the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656), the ROCOTRANSP project (PID2019-106702RB-C21 / AEI /10.13039/501100011033)) and the EU project CANOPIES (H2020- ICT-2020-2-101016906) Peer Reviewed Postprint (author's final draft) 2021 Conference report Laplaza, J. [et al.]. Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases. A: IEEE International Symposium on Robot and Human Interactive Communication. "Proceeding of 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)". 2021, p. 161-166. ISBN 978-1-6654-0492-1. DOI 10.1109/RO-MAN50785.2021.9515402. 978-1-6654-0492-1 https://hdl.handle.net/2117/355123 10.1109/RO-MAN50785.2021.9515402 eng https://ieeexplore.ieee.org/document/9515402 info:eu-repo/grantAgreement/MINECO/2PE/MDM-2016-0656 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106702RB-C21/ES/COLABORACION ROBOT-HUMANO PARA EL TRANSPORTE Y ENTREGA DE MERCANCIAS/ info:eu-repo/grantAgreement/EC/H2020/101016906/EU/A Collaborative Paradigm for Human Workers and Multi-Robot Teams in Precision Agriculture Systems/CANOPIES http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Attribution-NonCommercial-NoDerivs 3.0 Spain 6 p. application/pdf