Emre Uğur
Impact in
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- Robot Manipulation and Learning
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics
- AI-based Problem Solving and Planning
Papers in
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- Robot Manipulation and Learning 44
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- Reinforcement Learning in Robotics 30
- AI-based Problem Solving and Planning 9
- Evolutionary Algorithms and Applications 6
- Co-authors
- Erol Şahi̇n (19 shared papers)Erhan Öztop (28 shared papers)Justus Piater (14 shared papers)Maya Çakmak (5 shared papers)Mehmet R. Doğar (5 shared papers)Göktürk Üçoluk (1 shared paper)Yukie Nagai (7 shared papers)Lorenzo Jamone (3 shared papers)
In The Last Decade
Emre Uğur
69 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 90
- Control and Systems Engineering 805
- Artificial Intelligence 732
- Computer Vision and Pattern Recognition 321
- Cognitive Neuroscience 252
- Social Psychology 253
Countries citing papers authored by Emre Uğur
This map shows the geographic impact of Emre Uğur's research. 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 Emre Uğur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emre Uğur more than expected).
Fields of papers citing papers by Emre Uğur
This network shows the impact of papers produced by Emre Uğur. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Emre Uğur. The network helps show where Emre Uğur may publish in the future.
Co-authors
The 25 scholars most cited alongside Emre Uğur, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 255 | |
| 2 | 2016 | 124 | |
| 3 | 2011 | 74 | |
| 4 | 2018 | 62 | |
| 5 | 2015 | 58 | |
| 6 | 2015 | 57 | |
| 7 | 2010 | 54 | |
| 8 | 2017 | 52 | |
| 9 | 2007 | 50 | |
| 10 | 2012 | 48 | |
| 11 | 2023 | 41 | |
| 12 | 2019 | 29 | |
| 13 | 2019 | 27 | |
| 14 | 2012 | 27 | |
| 15 | 2007 | 26 | |
| 16 | 2007 | 25 | |
| 17 | Affordance learning from range data for multi-step planning. | 2009 | 25 |
| 18 | 2014 | 23 | |
| 19 | 2021 | 20 | |
| 20 | 2019 | 19 |
About Emre Uğur
Emre Uğur is a scholar working on Control and Systems Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Social Psychology and Cognitive Neuroscience, having authored 74 papers that have together received 1.4k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (44 papers), Reinforcement Learning in Robotics (30 papers), Human Pose and Action Recognition (10 papers), Action Observation and Synchronization (10 papers), Robotic Locomotion and Control (10 papers), AI-based Problem Solving and Planning (9 papers), Child and Animal Learning Development (9 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Control and Systems Engineering (805 citations), Artificial Intelligence (732 citations), Computer Vision and Pattern Recognition (321 citations), Cognitive Neuroscience (252 citations) and Social Psychology (253 citations). Emre Uğur has collaborated with scholars based in Türkiye, Japan and Austria. Frequent co-authors include Erol Şahi̇n, Erhan Öztop, Justus Piater, Maya Çakmak, Mehmet R. Doğar, Göktürk Üçoluk, Yukie Nagai, Lorenzo Jamone, José Santos-Victor and Alexandre Bernardino. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Cognitive and Developmental Systems, Adaptive Behavior, Robotica and Advanced Robotics.
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.