Frederik Ebert
Impact in
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
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- Tactile and Sensory Interactions
Papers in
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- Reinforcement Learning in Robotics 3
- Domain Adaptation and Few-Shot Learning 2
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- Robot Manipulation and Learning 2
- Co-authors
- Chelsea Finn (5 shared papers)Sergey Levine (5 shared papers)Dinesh Jayaraman (2 shared papers)Mayur Mudigonda (1 shared paper)Roberto Calandra (1 shared paper)Stephen Tian (2 shared papers)Alex X. Lee (1 shared paper)Aviral Kumar (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United States
In The Last Decade
Frederik Ebert
5 papers receiving 108 citations
Peers
Comparison fields: 5 of 29
- Control and Systems Engineering 67
- Cognitive Neuroscience 46
- Artificial Intelligence 37
- Computer Vision and Pattern Recognition 23
- Human-Computer Interaction 6
Countries citing papers authored by Frederik Ebert
This map shows the geographic impact of Frederik Ebert'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 Frederik Ebert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frederik Ebert more than expected).
Fields of papers citing papers by Frederik Ebert
This network shows the impact of papers produced by Frederik Ebert. 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 Frederik Ebert. The network helps show where Frederik Ebert may publish in the future.
Co-authors
The 13 scholars most cited alongside Frederik Ebert, 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 | 74 | |
| 2 | 2023 | 18 | |
| 3 | 2018 | 15 | |
| 4 | Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors | 2020 | 7 |
| 5 | Model-Based Visual Planning with Self-Supervised Functional Distances | 2021 | 1 |
About Frederik Ebert
Frederik Ebert is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Biomedical Engineering, having authored 5 papers that have together received 115 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Multimodal Machine Learning Applications (2 papers), Robot Manipulation and Learning (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Muscle activation and electromyography studies (1 paper), Advanced Sensor and Energy Harvesting Materials (1 paper) and Tactile and Sensory Interactions (1 paper). The work is most often cited by research in Control and Systems Engineering (67 citations), Cognitive Neuroscience (46 citations), Artificial Intelligence (37 citations), Computer Vision and Pattern Recognition (23 citations) and Human-Computer Interaction (6 citations). Frederik Ebert has collaborated with scholars based in United States. Frequent co-authors include Chelsea Finn, Sergey Levine, Dinesh Jayaraman, Mayur Mudigonda, Roberto Calandra, Stephen Tian, Alex X. Lee, Aviral Kumar, Sudeep Dasari and Anikait Singh. Their work appears in journals such as International Conference on Learning Representations, arXiv (Cornell University) and Neural Information Processing Systems.
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.