End-to-end training of deep visuomotor policies

700 indexed citations

Abstract

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About

This paper, published in 2016, received 700 indexed citations. Written by Sergey Levine, Chelsea Finn, Trevor Darrell and Pieter Abbeel covering the research area of Control and Systems Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (414 citations), Control and Systems Engineering (313 citations) and Computer Vision and Pattern Recognition (236 citations). Published in Journal of Machine Learning Research.

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Countries where authors are citing End-to-end training of deep visuomotor policies

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This map shows the geographic impact of End-to-end training of deep visuomotor policies. 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 End-to-end training of deep visuomotor policies with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites End-to-end training of deep visuomotor policies more than expected).

Fields of papers citing End-to-end training of deep visuomotor policies

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of End-to-end training of deep visuomotor policies. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the End-to-end training of deep visuomotor policies.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w7187005.

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