Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking

Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig, Andriy Sarabakha, Erdal Kayacan

    Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


    In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves high-accuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt.

    Original languageEnglish
    Title of host publication2019 18th European Control Conference, ECC 2019
    Number of pages8
    Publication dateJun 2019
    Article number8796140
    ISBN (Electronic)9783907144008
    Publication statusPublished - Jun 2019
    Event18th European Control Conference, ECC 2019 - Naples, Italy
    Duration: 25 Jun 201928 Jun 2019


    Conference18th European Control Conference, ECC 2019
    SponsorEuropean Control Assoication (EUCA)


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