The affordance theory provides a biology-inspired approach to enable a robot to act, think and develop like human beings. Based on existing affordance relationships, a robot understands its environment and task in terms of potential actions that it can execute. Deep learning makes it possible for a robot to perceive the environment in an efficient manner. As a result, affordance-based perception together with deep learning provides a possible solution for a robot to provide good service to us. However, affordance knowledge can not be gained just by visual perception and a single object might have multiply affordances. In this paper, we propose a novel framework to combine affordance knowledge and visual perception. Our method has the following features: (i) map human instructions into affordance knowledge; (ii) perceive the environment based on deep neural networks and associate each object with its affordances. In our experiments, a humanoid robot NAO is used and the results demonstrate that affordance knowledge can improve robotic understanding based on deep learning.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Using Affordances to Improve Robotic Understanding Based on Deep Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Yi, Chang’an (Autor:in) / Chen, Haotian (Autor:in) / Zhong, Jingtang (Autor:in) / Liu, Xianguo (Autor:in) / Hu, Xiaosheng (Autor:in) / Xu, Yonghui (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Using Affordances to Improve Robotic Understanding Based on Deep Learning

    Yi, Chang'an / Chen, Haotian / Zhong, Jingtang et al. | British Library Conference Proceedings | 2022


    Using Affordances to Improve Robotic Understanding Based on Deep Learning

    Yi, Chang’an / Chen, Haotian / Zhong, Jingtang et al. | TIBKAT | 2022


    Using Object Affordances to Improve Object Recognition

    Castellini, Claudio / Tommasi, Tatiana / Noceti, Nicoletta et al. | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2011

    Freier Zugriff

    Robotic control using deep learning

    CHRISTOPHER JASON PAXTON / KEI KASE / DIETER FOX | Europäisches Patentamt | 2021

    Freier Zugriff