Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A neural network-based exploratory learning and motor planning system for co-robots


    Beteiligte:

    Erscheinungsdatum :

    2015-01-01


    Anmerkungen:

    doi:10.3389/fnbot.2015.00007



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    A neural network based self-learning control system for underwater robots

    Fujii, T. / Ura, T. / Sutoh, T. et al. | British Library Online Contents | 1995


    Neural Network Control System for Underwater Robots

    Dyda, A. A. / Os kin, D. A. / International Federation of Automatic Control | British Library Conference Proceedings | 2005



    Q-Learnıng Based Real Tıme Path Plannıng for Mobıle Robots

    Halil Cetin / Akif Durdu / M. Fatih Aslan et al. | BASE | 2019

    Freier Zugriff

    Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network

    Navone, Alessandro / Martini, Mauro / Angarano, Simone et al. | BASE | 2023

    Freier Zugriff