Attention deep learning based model for predicting the 3D Human Body Pose using the Robot Human Handover Phases
10.1109/RO-MAN50785.2021.9515402
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/355123
Tipus de documentText en actes de congrés
Data publicació2021
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteCOLABORACION ROBOT-HUMANO PARA EL TRANSPORTE Y ENTREGA DE MERCANCIAS (AEI-PID2019-106702RB-C21)
CANOPIES - A Collaborative Paradigm for Human Workers and Multi-Robot Teams in Precision Agriculture Systems (EC-H2020-101016906)
CANOPIES - A Collaborative Paradigm for Human Workers and Multi-Robot Teams in Precision Agriculture Systems (EC-H2020-101016906)
Abstract
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.
Descripció
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CitacióLaplaza, 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.
ISBN978-1-6654-0492-1
Versió de l'editorhttps://ieeexplore.ieee.org/document/9515402
Col·leccions
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos [576]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.500]
- VIS - Visió Artificial i Sistemes Intel·ligents - Ponències/Comunicacions de congressos [292]
- ROBiri - Grup de Percepció i Manipulació Robotitzada de l'IRI - Ponències/Comunicacions de congressos [252]
- Doctorat en Automàtica, Robòtica i Visió - Ponències/Comunicacions de congressos [166]
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