Humans are error-prone in the presence of multi- ple similar tasks. While Human-Robot Collaboration (HRC) brings the advantage of combining the superiority of both humans and robots in their respective talents, it also requires the robot to communicate the task goal clearly to the human col- laborator. We formalize such problems in interactive assembly tasks with hidden goal Markov decision processes (HGMDPs) to enable the symbiosis of human intention recognition and robot intention expression. In order to avoid the prohibitive computa- tional requirements, we provide a myopic heuristic along with a feature-based state abstraction method for assembly tasks to approximate the solution of the resulting HGMDP. A user study with human subjects in round-based LEGO assembly tasks shows that our algorithm improves HRC and helps the human collaborators when the task goal is unclear to them.


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    Title :

    Legible Action Selection in Human-Robot Collaboration


    Contributors:

    Publication date :

    2017-09-06


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    004 / 629 / 620




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