Rico Jonschkowski
Impact in
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- Robot Manipulation and Learning
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- Robotic Path Planning Algorithms
- Advanced Neural Network Applications
- Advanced Vision and Imaging
Papers in
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- Reinforcement Learning in Robotics 7
- Domain Adaptation and Few-Shot Learning 4
- Machine Learning and Algorithms 3
- Data Stream Mining Techniques 1
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- Robot Manipulation and Learning 6
- Co-authors
- Oliver Brock (9 shared papers)Sebastian Höfer (5 shared papers)Clemens Eppner (4 shared papers)Roberto Martín-Martín (4 shared papers)Vincent Wall (3 shared papers)Anelia Angelova (1 shared paper)Kurt Konolige (1 shared paper)Antonin Raffin (1 shared paper)
- Journals
- Autonomous Robots (2 papers)elib (German Aerospace Center) (1 paper)
- Partner nations
- GermanyUnited StatesFrance
In The Last Decade
Rico Jonschkowski
12 papers receiving 376 citations
Peers
Comparison fields: 5 of 50
- Control and Systems Engineering 235
- Computer Vision and Pattern Recognition 174
- Aerospace Engineering 125
- Industrial and Manufacturing Engineering 47
- Human-Computer Interaction 22
Countries citing papers authored by Rico Jonschkowski
This map shows the geographic impact of Rico Jonschkowski'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 Rico Jonschkowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rico Jonschkowski more than expected).
Fields of papers citing papers by Rico Jonschkowski
This network shows the impact of papers produced by Rico Jonschkowski. 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 Rico Jonschkowski. The network helps show where Rico Jonschkowski may publish in the future.
Co-authors
The 14 scholars most cited alongside Rico Jonschkowski, 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 | 2016 | 91 | |
| 2 | 2020 | 82 | |
| 3 | 2015 | 75 | |
| 4 | 2016 | 50 | |
| 5 | 2017 | 39 | |
| 6 | 2018 | 25 | |
| 7 | 2014 | 22 | |
| 8 | End-to-End Learnable Histogram Filters | 2017 | 10 |
| 9 | 2019 | 5 | |
| 10 | Scaling Up Multi-Task Robotic Reinforcement Learning | 2021 | 3 |
| 11 | Unsupervised Learning of State Representations for Multiple Tasks | 2017 | 3 |
| 12 | Found by NEMO: Unsupervised Object Detection from Negative Examples and Motion | 2018 | 1 |
About Rico Jonschkowski
Rico Jonschkowski is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering and Aerospace Engineering, having authored 12 papers that have together received 406 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Robot Manipulation and Learning (6 papers), Domain Adaptation and Few-Shot Learning (4 papers), Machine Learning and Algorithms (3 papers), Modular Robots and Swarm Intelligence (3 papers), Robotics and Sensor-Based Localization (2 papers), Advanced Neural Network Applications (1 paper) and Data Stream Mining Techniques (1 paper). The work is most often cited by research in Control and Systems Engineering (235 citations), Computer Vision and Pattern Recognition (174 citations), Aerospace Engineering (125 citations), Industrial and Manufacturing Engineering (47 citations) and Human-Computer Interaction (22 citations). Rico Jonschkowski has collaborated with scholars based in Germany, United States and France. Frequent co-authors include Oliver Brock, Sebastian Höfer, Clemens Eppner, Roberto Martín-Martín, Vincent Wall, Anelia Angelova, Kurt Konolige, Antonin Raffin, Freek Stulp and Yevgen Chebotar. Their work appears in journals such as Autonomous Robots and elib (German Aerospace Center).
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.