Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot.


    Access

    Download


    Export, share and cite



    Title :

    Learning to Avoid Obstacles With Minimal Intervention Control


    Contributors:

    Publication date :

    2020-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Redundant Robot Can Avoid Obstacles

    Homayoun, Seraji / Colbaugh, Richard / Glass, Kristin | NTRS | 1991



    Planning Motions To Avoid Moving Obstacles

    Fiorini, Paolo / Shiller, Zvi | NTRS | 1995


    Towards Learning Obstacles to Avoid Collisions in Autonomous Robot Navigation

    Sanchez-Garcia, Angel J. / Rios-Figueroa, Homero V. / Limon-Riano, Xavier et al. | IEEE | 2019


    ANN Method for Control of Robots to Avoid Obstacles

    Ciupan, Emilia / Lungu, Florin / Ciupan, Cornel | BASE | 2014

    Free access