Robin Strudel
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
Papers in
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- Multimodal Machine Learning Applications 2
- Advanced Neural Network Applications 1
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- Reinforcement Learning in Robotics 3
- Domain Adaptation and Few-Shot Learning 2
- Evolutionary Algorithms and Applications 1
- Co-authors
- Ivan Laptev (4 shared papers)Cordelia Schmid (4 shared papers)Ricardo Garcı́a (1 shared paper)Shizhe Chen (1 shared paper)Josef Šivic (1 shared paper)Jean Ponce (1 shared paper)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)arXiv (Cornell University) (1 paper)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (1 paper)
- Partner nations
- FranceUnited States
In The Last Decade
Robin Strudel
5 papers receiving 48 citations
Peers
Comparison fields: 5 of 31
- Computer Vision and Pattern Recognition 28
- Health Informatics 1
- Artificial Intelligence 24
- Industrial and Manufacturing Engineering 7
- Control and Systems Engineering 15
Countries citing papers authored by Robin Strudel
This map shows the geographic impact of Robin Strudel'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 Robin Strudel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robin Strudel more than expected).
Fields of papers citing papers by Robin Strudel
This network shows the impact of papers produced by Robin Strudel. 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 Robin Strudel. The network helps show where Robin Strudel may publish in the future.
Co-authors
The 6 scholars most cited alongside Robin Strudel, 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 | 2019 | 35 | |
| 2 | 2021 | 13 | |
| 3 | Combining learned skills and reinforcement learning for robotic manipulations. | 2019 | 2 |
| 4 | 2023 | 2 | |
| 5 | 2022 | 2 |
About Robin Strudel
Robin Strudel is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Aerospace Engineering and Industrial and Manufacturing Engineering, having authored 5 papers that have together received 54 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Robot Manipulation and Learning (2 papers), Multimodal Machine Learning Applications (2 papers), Evolutionary Algorithms and Applications (1 paper), Robotics and Sensor-Based Localization (1 paper), Manufacturing Process and Optimization (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (28 citations), Health Informatics (1 citation), Artificial Intelligence (24 citations), Industrial and Manufacturing Engineering (7 citations) and Control and Systems Engineering (15 citations). Robin Strudel has collaborated with scholars based in France and United States. Frequent co-authors include Ivan Laptev, Cordelia Schmid, Ricardo Garcı́a, Shizhe Chen, Josef Šivic and Jean Ponce. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), arXiv (Cornell University), 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and HAL (Le Centre pour la Communication Scientifique Directe).
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.