Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/137704
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Type: Conference paper
Title: What Can Robotics Research Learn from Computer Vision Research?
Author: Corke, P.
Dayoub, F.
Hall, D.
Skinner, J.
Sünderhauf, N.
Citation: Proceedings of the 19th International Symposium of Robotic Research (ISRR 2019), as published in Springer Proceedings in Advanced Robotics, 2022 / Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (ed./s), vol.20, pp.987-1003
Publisher: Springer
Publisher Place: Cham, Switzerland
Issue Date: 2022
Series/Report no.: Springer Proceedings in Advanced Robotics; 20
ISBN: 9783030954581
ISSN: 2511-1256
2511-1264
Conference Name: International Symposium of Robotic Research (ISRR) (6 Oct 2019 - 10 Oct 2019 : Hanoi, Vietnam)
Editor: Asfour, T.
Yoshida, E.
Park, J.
Christensen, H.
Khatib, O.
Statement of
Responsibility: 
Peter Corke, Feras Dayoub, David Hall, John Skinner, Niko Sünderhauf
Abstract: The fields of computer vision and robotics are both children of the artificial intelligence program that was spawned by the Dartmouth Conference in 1956. In recent decades the fields have diverged in terms of conferences and journals, research methodology and research rate. From a robotics perspective it seems that computer vision is in the fast lane while robotics is stuck in the slow lane. Roboticists hold a fundamental belief in the importance of experimentation but could it be that experiments are actually holding us back? Or is it that we are doing experiments poorly?
Keywords: Robotics; Computer vision; Research methodology
Rights: © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
DOI: 10.1007/978-3-030-95459-8_61
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
Published version: https://link.springer.com/book/10.1007/978-3-030-95459-8
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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