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.


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    Title :

    Using Affordances to Improve Robotic Understanding Based on Deep Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Yi, Chang’an (author) / Chen, Haotian (author) / Zhong, Jingtang (author) / Liu, Xianguo (author) / Hu, Xiaosheng (author) / Xu, Yonghui (author)

    Conference:

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



    Publication date :

    2022-03-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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