The limits and potentials of deep learning for robotics

309 indexed citations

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This paper, published in 2018, received 309 indexed citations. Written by Niko Sünderhauf, Oliver Brock, Walter J. Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard and Michael Milford covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (127 citations), Computer Vision and Pattern Recognition (116 citations) and Control and Systems Engineering (80 citations). Published in QUT ePrints (Queensland University of Technology).

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Countries where authors are citing The limits and potentials of deep learning for robotics

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This map shows the geographic impact of The limits and potentials of deep learning for robotics. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by The limits and potentials of deep learning for robotics with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The limits and potentials of deep learning for robotics more than expected).

Fields of papers citing The limits and potentials of deep learning for robotics

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of The limits and potentials of deep learning for robotics. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The limits and potentials of deep learning for robotics.

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This paper is also available at doi.org/w2419553.

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