Publisher Copyright: © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). ; In most autonomous robot approaches, the individual robot’s goals and cooperation behavior are fixed during the design. Moreover, the robot’s design may limit its ability to perform other than initially planned tasks. This leaves little room for novel dynamic cooperation where new (joint) actions could be formed or goals adjusted after deployment. In this paper, we address how situational context augmented with peer modeling can foster cooperation opportunity identification and cooperation planning. As a practical contribution, we introduce our new software architecture that enables developing, training, testing, and deploying dynamic cooperation solutions for diverse autonomous robots. The presented architecture operates in three different worlds: in the Real World with real robots, in the 3D Virtual World by emulating the real environments and robots, and in an abstract 2D Block World that fosters developing and studying large-scale cooperation scenarios. Feedback loops among these three worlds bring data from one world to another and provide valuable information to improve cooperation solutions. ; Peer reviewed


    Access

    Download


    Export, share and cite



    Title :

    Towards novel and intentional cooperation of diverse autonomous robots : An architectural approach



    Publication date :

    2021-01-01


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Stigmergic cooperation of autonomous robots

    Zieliński, Cezary | Online Contents | 2009


    Stigmergic cooperation of autonomous robots

    Zieliński, Cezary | Online Contents | 2009


    Social Robots as Intentional Agents

    Thellman, Sam | BASE | 2021

    Free access


    Do We Adopt the Intentional Stance Toward Humanoid Robots?

    Serena Marchesi / Davide Ghiglino / Francesca Ciardo et al. | BASE | 2019

    Free access