Abstract: From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation -- Constraint Action Templates (CATs) -- combine the advantages of Action Templates (Leidner, 2019) and Shared Control Templates (Quere, 2020). We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode.


    Access

    Download


    Export, share and cite



    Title :

    CATs: Task Planning for Shared Control of Assistive Robots with Variable Autonomy


    Contributors:

    Conference:

    2022 ; Philadelphia, USA



    Publication date :

    2022



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Feasibility Checks for Safe Shared Control with Variable Autonomy in Assistive Robotics

    Bustamante Gomez, Samuel / Rodriguez Brena, Ismael Valentin / Quere, Gabriel et al. | German Aerospace Center (DLR) | 2022

    Free access

    Adaptive degrees of freedom for shared control of assistive robots

    Goldau, Felix Ferdinand / Universität Bremen | TIBKAT | 2024

    Free access

    TASK ALLOCATION AND VARIABLE AUTONOMY LEVELS

    SANE HARSHAD S / UNNIKRISHNAN SURAJ / CHEREPINSKY IGOR et al. | European Patent Office | 2017

    Free access

    Task allocation and variable autonomy levels

    SANE HARSHAD S / UNNIKRISHNAN SURAJ / CHEREPINSKY IGOR et al. | European Patent Office | 2019

    Free access

    Shared Control Templates for Assistive Robotics

    Quere, Gabriel / Hagengruber, Annette / Iskandar, Maged Samuel Zakri et al. | German Aerospace Center (DLR) | 2020

    Free access