One of the big challenges in robotics is to endow agents with autonomous and adaptive capabilities. With this purpose, we embedded a cerebellum-based control system into a humanoid robot that becomes capable of handling dynamical external and internal complexity. The cerebellum is the area of the brain that coordinates and predicts the body movements throughout the body-environment interactions. Different biologically plausible cerebellar models are available in literature and have been employed for motor learning and control of simplified objects. We built the canonical cerebellar microcircuit by combining machine learning and computational neuroscience techniques. The control system is composed of the adaptive cerebellar module and a classic control method; their combination allows a fast adaptive learning and robust control of the robotic movements when external disturbances appear. The control structure is built offline, but the dynamic parameters are learned during an online-phase training. The aforementioned adaptive control system has been tested in the Neuro-robotics Platform with the virtual humanoid robot iCub. In the experiment, the robot iCub has to balance with the hand a table with a ball running on it. In contrast with previous attempts of solving this task, the proposed neural controller resulted able to quickly adapt when the internal and external conditions change. Our bio-inspired and flexible control architecture can be applied to different robotic configurations without an excessive tuning of the parameters or customization. The cerebellum-based control system is indeed able to deal with changing dynamics and interactions with the environment. Important insights regarding the relationship between the bio-inspired control system functioning and the complexity of the task to be performed are obtained.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment


    Beteiligte:

    Erscheinungsdatum :

    2019-01-01


    Anmerkungen:

    Capolei , M C , Angelidis , E , Falotico , E , Lund , H H & Tolu , S 2019 , ' A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment ' , Frontiers in Neurorobotics , vol. 13 , 70 . https://doi.org/10.3389/fnbot.2019.00070



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment

    Capolei, Marie Claire / Andersen, Nils Axel / Lund, Henrik Hautop et al. | BASE | 2020

    Freier Zugriff

    ROBOT, HUMANOID ROBOT, AND ROBOT FALL CONTROL METHOD

    KAMON MASAYUKI / KARASUYAMA JUNICHI | Europäisches Patentamt | 2023

    Freier Zugriff

    Humanoid robot

    AMINO AZUSA / ICHINOSE RYOKO / NAKAMURA RYOSUKE et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    HUMANOID ROBOT

    AMINO AZUSA / ICHINOSE RYOKO / NAKAMURA RYOSUKE et al. | Europäisches Patentamt | 2019

    Freier Zugriff

    Humanoid robot

    LIU HANQIAO / JIANG FEILONG / SHEN ZHONGFEI et al. | Europäisches Patentamt | 2023

    Freier Zugriff