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    Interactive Text2Pickup Networks for Natural Language-Based Human-Robot Collaboration  
    Ahn, Hyemin(Seoul National University)
    United States | IEEE Robotics and Automation Letters
    2018-07-04 | 바로가기
    Heating_systems, Robots, Task_analysis
    Cited by 6

    ■  View full text

    IEEE Robotics and Automation Letters

    Date of Publication: 04 July 2018

    https://doi.org/10.1109/LRA.2018.2852786

     

    ■  Researchers

    Hyemin Ahn1, Sungjoon Choi2, Nuri Kim1, Geonho Cha1, Songhwai Oh1

     

    1  Department of Electrical and Computer Engineering and ASRI, Seoul National University

    2  Kakao Brain

     

    ■  Abstract

    In this letter, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration that enables an effective interaction with a human user despite the ambiguity in user's commands. We focus on the task where a robot is expected to pick up an object instructed by a human, and to interact with the human when the given instruction is vague. The proposed network understands the command from the human user and estimates the position of the desired object first. To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated. The user's answer to the question is combined with the initial command and given back to the network, resulting in more accurate estimation. The experiment results show that given unambiguous commands, the proposed method can estimate the position of the requested object with an accuracy of 98.49% based on the test dataset. Given ambiguous language commands, we show that the accuracy of the pick up task increases by 1.94 times after incorporating the information obtained from the interaction.

     

     

    ■  Conclusion

    In this letter, we have proposed the Interactive Text2Pickup (IT2P) network for picking up the requested object when a human language command is given. The IT2P network interacts with a human user when an ambiguous language command is provided, in order to resolve the ambiguity. By understanding the given language command, the proposed network can successfully predict the position of the desired object and the uncertainty associated with the predicted target position. In order to mitigate the ambiguity in the language command, the network generates a suitable question to ask the human user. We have shown that the proposed IT2P network can efficiently interact with humans by asking a question appropriate to the given situation. The proposed network is applied to a Baxter robot and the collaboration between a real robot and a human user has been conducted. We believe that the proposed method, which can efficiently interact with humans by asking questions based on the uncertainty in estimation, will enable more natural collaboration between a human and a robot.

     

     

     

     

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