Imitation learning could enable non-expert users to teach new skills to robots in an interactive and intuitive way. Still, when teaching a task, it is often difficult to grasp what the robot knows or to assess if a correct task representation is being formed. To address this problem, suitable online feedback should be given by the robot to explain its perceptual beliefs.

    Here, we introduce an explainable design for human-robot interaction during learning by demonstration of simple kitchen tasks in Augmented Reality. To communicate the robot feedback during the demonstration two modalities are explored: one purely AR-based (AR-XAI), by which the perceptual beliefs of the robot are interactively overlaid on the shared workspace, as perceived by the teacher; and one more human-like (gaze-speech), where the robot’s understanding is signaled by gaze following and verbal utterances. We conducted a user study to assess which modality is more effective in shaping the user perception and to evaluate whether a combination of holographic and embodied social cues would further improve the user experience. Our results show that the multi-modal combination is indeed best appreciated and contributes to a better perception of the robot, especially in non-experts.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Explainable Human-Robot Interaction for Imitation Learning in Augmented Reality


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Piazza, Cristina (Herausgeber:in) / Capsi-Morales, Patricia (Herausgeber:in) / Figueredo, Luis (Herausgeber:in) / Keppler, Manuel (Herausgeber:in) / Schütze, Hinrich (Herausgeber:in) / Belardinelli, Anna (Autor:in) / Wang, Chao (Autor:in) / Gienger, Michael (Autor:in)

    Kongress:

    International Workshop on Human-Friendly Robotics ; 2023 ; Munich, Germany September 20, 2023 - September 21, 2023



    Erscheinungsdatum :

    10.03.2024


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Human-Robot Collaboration: An Augmented Reality Toolkit for Bi-Directional Interaction

    Graziano Carriero / Nicolas Calzone / Monica Sileo et al. | BASE | 2023

    Freier Zugriff

    Human motion imitation robot

    Thaker, Swapnil Amit | BASE | 2019

    Freier Zugriff

    Concepts for End-to-end Augmented Reality based Human-Robot Interaction Systems

    Puljiz, David / Hein, Björn | BASE | 2019

    Freier Zugriff

    Bidirectional Human-Robot Learning: Imitation and Skill Improvement

    Sousa Ewerton, Marco Antônio | TIBKAT | 2020


    Intuitive and Safe Interaction in Multi-User Human Robot Collaboration Environments through Augmented Reality Displays

    Georgios Tsamis / Georgios Chantziaras / Dimitrios Giakoumis et al. | BASE | 2021

    Freier Zugriff