Human-like trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for character animation based on training human-like models directly on motion capture data. Such methods proved effective in virtual environments, mainly focusing on trajectory visualization. This paper presents ADHERENT, a system architecture integrating machine-learning methods used in computer graphics with whole-body control methods employed in robotics to generate and stabilize human-like trajectories for humanoid robots. Leveraging human motion capture locomotion data, ADHERENT yields a general footstep planner, including forward, sideways, and backward walking trajectories that blend smoothly from one to another. Furthermore, at the joint configuration level, ADHERENT computes data-driven whole-body postural reference trajectories coherent with the generated footsteps, thus increasing the human likeness of the resulting robot motion. Extensive validations of the proposed architecture are presented with both simulations and real experiments on the iCub humanoid robot, thus demonstrating ADHERENT to be robust to varying step sizes and walking speeds.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots



    Erscheinungsdatum :

    2022-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Dynamic Whole-Body Control of Unstable Wheeled Humanoid Robots

    Zambella, Grazia / Lentini, Gianluca / Garabini, Manolo et al. | BASE | 2019

    Freier Zugriff

    Whole-body hierarchical motion and force control for humanoid robots

    Liu, M. | British Library Online Contents | 2016


    Human- or object-like? Cognitive anthropomorphism of humanoid robots

    Sacino, Alessandra / Cocchella, Francesca / De Vita, Giulia et al. | BASE | 2022

    Freier Zugriff

    Human- or object-like? Cognitive anthropomorphism of humanoid robots

    Sacino, Alessandra / Cocchella, Francesca / De Vita, Giulia et al. | BASE | 2022

    Freier Zugriff