Neural-Fly enables rapid learning for agile flight in strong winds

148 indexed citations

Abstract

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This paper, published in 2022, received 148 indexed citations. Written by Michael O’Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue and Soon‐Jo Chung covering the research area of Statistical and Nonlinear Physics, Aerospace Engineering and Artificial Intelligence. It is primarily cited by scholars working on Control and Systems Engineering (76 citations), Aerospace Engineering (47 citations) and Artificial Intelligence (39 citations). Published in Science Robotics.

Countries where authors are citing Neural-Fly enables rapid learning for agile flight in strong winds

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This map shows the geographic impact of Neural-Fly enables rapid learning for agile flight in strong winds. 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 Neural-Fly enables rapid learning for agile flight in strong winds with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neural-Fly enables rapid learning for agile flight in strong winds more than expected).

Fields of papers citing Neural-Fly enables rapid learning for agile flight in strong winds

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Neural-Fly enables rapid learning for agile flight in strong winds. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Neural-Fly enables rapid learning for agile flight in strong winds.

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This paper is also available at doi.org/10.1126/scirobotics.abm6597.

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