Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141405
Title: Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking
Authors: Zhou, Siqi
Sarabakha, Andriy
Kayacan, Erdal
Helwa, Mohamed K.
Schoellig, Angela P.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Zhou, S., Sarabakha, A., Kayacan, E., Helwa, M. K., & Schoellig, A. P. (2019). Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking. Proceedings of 2019 18th European Control Conference (ECC), 1-8. doi:10.23919/ecc.2019.8796140
Abstract: 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.
URI: https://hdl.handle.net/10356/141405
ISBN: 978-1-7281-1314-2
DOI: 10.23919/ECC.2019.8796140
Rights: © 2019 EUCA. All rights reserved. This paper was published in Proceedings of 2019 18th European Control Conference (ECC) and is made available with permission of EUCA.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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