Trabajo presentado en la 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), celebrada en Nápoles (Italia) del 31 de agosto al 4 de septiembre de 2020 ; Industrial robots are evolving to work closely with humans in shared spaces. Hence, robotic tasks are increasingly shared between humans and robots in collaborative settings. To enable a fluent human robot collaboration, robots need to predict and respond in real-time to worker's intentions. We present a method for early decision using force infor-mation. Forces are provided naturally by the user through the manipulation of a shared object in a collaborative task. The proposed algorithm uses a recurrent neural network to recognize operator's intentions. The algorithm is evaluated in terms of action recognition on a force dataset. It excels at detecting intentions when partial data is provided, enabling early detection and facilitating a quick robot reaction.


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    Title :

    Recurrent neural networks for inferring intentions in shared tasks for industrial collaborative robots



    Publication date :

    2020-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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