Sebastian Höfer
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 10%
- Mechanical Engineering
- Artificial Intelligence
- Co-authors
- Oliver BrockRoberto Martín-MartínRico JonschkowskiClemens EppnerVincent WallJürgen BeyererJonathan BalzerAlexey Pak
- Topics
- Robot Manipulation and Learning (6 papers)Reinforcement Learning in Robotics (6 papers)Modular Robots and Swarm Intelligence (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionControl and Systems EngineeringIndustrial and Manufacturing Engineering
- Partner nations
- GermanyUnited StatesSaudi Arabia
In The Last Decade
Sebastian Höfer
21 papers receiving 414 citations
Peers
Comparison fields: 5 of 61
- Control and Systems Engineering 223
- Computer Vision and Pattern Recognition 216
- Aerospace Engineering 107
- Mechanical Engineering 91
- Artificial Intelligence 90
Countries citing papers authored by Sebastian Höfer
This map shows the geographic impact of Sebastian Höfer'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 Sebastian Höfer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Höfer more than expected).
Fields of papers citing papers by Sebastian Höfer
This network shows the impact of papers produced by Sebastian Höfer. 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 Sebastian Höfer. The network helps show where Sebastian Höfer may publish in the future.
Co-authorship network of co-authors of Sebastian Höfer
This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Höfer. A scholar is included among the top collaborators of Sebastian Höfer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sebastian Höfer. Sebastian Höfer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 29 | |
| 2 | 81 | |
| 3 | 3 | |
| 4 | 24 | |
| 5 | Unsupervised Learning of State Representations for Multiple Tasks | 3 |
| 6 | 38 | |
| 7 | 8 | |
| 8 | 23 | |
| 9 | 7 | |
| 10 | 91 | |
| 11 | 50 | |
| 12 | 3 | |
| 13 | 9 | |
| 14 | 9 | |
| 15 | 20 | |
| 16 | 1 | |
| 17 | 11 | |
| 18 | 1 | |
| 19 | Proposal of an Intrinsically Motivated System for Exploration of Sensorimotor State Spaces | 3 |
| 20 | 1 |
About Sebastian Höfer
Sebastian Höfer is a scholar working on Computer Vision and Pattern Recognition, Geology and Artificial Intelligence, having authored 21 papers that have together received 447 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (6 papers), Reinforcement Learning in Robotics (6 papers) and Modular Robots and Swarm Intelligence (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (216 citations), Control and Systems Engineering (223 citations) and Industrial and Manufacturing Engineering (57 citations). Sebastian Höfer has collaborated with scholars based in Germany, United States and Saudi Arabia. Frequent co-authors include Oliver Brock, Roberto Martín-Martín, Rico Jonschkowski, Clemens Eppner, Vincent Wall, Jürgen Beyerer, Jonathan Balzer, Alexey Pak, Melissa Mozifian and Ankur Handa. Their work appears in journals such as PLoS ONE, Autonomous Robots and IEEE Transactions on Automation Science and Engineering.
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.