Coline Devin
Impact in
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- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
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- Robot Manipulation and Learning
Papers in
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- Reinforcement Learning in Robotics 4
- Domain Adaptation and Few-Shot Learning 2
- Artificial Intelligence in Games 1
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- Multimodal Machine Learning Applications 2
- Image Processing and 3D Reconstruction 1
- Co-authors
- Sergey Levine (5 shared papers)Pieter Abbeel (2 shared papers)Abhishek Gupta (1 shared paper)Yuxuan Liu (1 shared paper)Vincent Vanhoucke (1 shared paper)Eric Jang (1 shared paper)Dinesh Jayaraman (1 shared paper)Abbas Abdolmaleki (1 shared paper)
- Journals
- arXiv (Cornell University) (3 papers)Neural Information Processing Systems (1 paper)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Coline Devin
6 papers receiving 71 citations
Peers
Comparison fields: 5 of 22
- Artificial Intelligence 56
- Control and Systems Engineering 33
- Computer Vision and Pattern Recognition 27
- Human-Computer Interaction 3
- Computer Science Applications 2
Countries citing papers authored by Coline Devin
This map shows the geographic impact of Coline Devin'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 Coline Devin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Coline Devin more than expected).
Fields of papers citing papers by Coline Devin
This network shows the impact of papers produced by Coline Devin. 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 Coline Devin. The network helps show where Coline Devin may publish in the future.
Co-authors
The 25 scholars most cited alongside Coline Devin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 47 | |
| 2 | Grasp2Vec: Learning Object Representations from Self-Supervised Grasping. | 2018 | 12 |
| 3 | SMiRL: Surprise Minimizing RL in Dynamic Environments | 2019 | 6 |
| 4 | 2022 | 4 | |
| 5 | Learning To Reach Goals Without Reinforcement Learning | 2019 | 3 |
| 6 | Compositional Plan Vectors | 2019 | 3 |
| 7 | 2025 | 0 |
About Coline Devin
Coline Devin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Management Science and Operations Research and Information Systems and Management, having authored 7 papers that have together received 75 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Multimodal Machine Learning Applications (2 papers), Robot Manipulation and Learning (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Scientific Computing and Data Management (1 paper), Image Processing and 3D Reconstruction (1 paper), Artificial Intelligence in Games (1 paper) and Computability, Logic, AI Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (56 citations), Control and Systems Engineering (33 citations), Computer Vision and Pattern Recognition (27 citations), Human-Computer Interaction (3 citations) and Computer Science Applications (2 citations). Coline Devin has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Sergey Levine, Pieter Abbeel, Abhishek Gupta, Yuxuan Liu, Vincent Vanhoucke, Eric Jang, Dinesh Jayaraman, Abbas Abdolmaleki, Glen Berseth and Trevor Darrell. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.