A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots. We leverage the information from past executions to learn the average duration of each action and a synergy coefficient representing the effect of an action performed by the human on the duration of the action performed by the robot (and vice versa). We implement the proposed method in a simulated scenario where both agents can access the same area simultaneously. Safety measures require the robot to slow down when the human is close, denoting a bad synergy of tasks operating in the same area. We show that our approach can learn such bad couplings so that a task planner can leverage this information to find better plans.


    Access

    Download


    Export, share and cite



    Title :

    Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration



    Publication date :

    2022-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Capability-based task allocation in human-robot collaboration

    Ranz, Fabian / Hummel, Vera / Sihn, Wilfried | BASE | 2017

    Free access

    Capability-based task allocation in human-robot collaboration

    Ranz, Fabian / Hummel, Vera / Sihn, Wilfried | BASE | 2017

    Free access

    Legible Action Selection in Human-Robot Collaboration

    Zhu, Huaijiang / Gabler, Volker / Wollherr, Dirk | BASE | 2017

    Free access


    HUMAN-ROBOT COLLABORATION

    FELIP LEON JAVIER / AHUJA NILESH / CAMPOS MACIAS LEOBARDO et al. | European Patent Office | 2021

    Free access