Philipp Schillinger

24 papers receiving 392 citations

Peers

Philipp Schillinger
Comparison fields: 5 of 40
  • Software 66
  • Computational Theory and Mathematics 143
  • Computer Vision and Pattern Recognition 142
  • Artificial Intelligence 156
  • Control and Systems Engineering 95
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Bruno Lacerda United Kingdom
Charles Lesire France
Alphan Ulusoy United States
V. A. Ziparo Italy
Tim Niemueller Germany
Arne Nordmann Germany
Geoffrey Biggs Japan
Martijn Rooker Austria
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Citations per year

Countries citing papers authored by Philipp Schillinger

Since Specialization
Citations

This map shows the geographic impact of Philipp Schillinger'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 Philipp Schillinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Schillinger more than expected).

Fields of papers citing papers by Philipp Schillinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philipp Schillinger. 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 Philipp Schillinger. The network helps show where Philipp Schillinger may publish in the future.

Co-authors

The 25 scholars most cited alongside Philipp Schillinger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Philipp Schillinger Line = papers co-authored together Philipp Schillinger links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018112
2 201670
3 201833
4 201631
5 201923
6 201622
7 201714
8 201811
9
Hierarchical LTL-Task MDPs for Multi-Agent Coordination through Auctioning and Learning
201910
10 202110
11 20169
12 20218
13 20187
14 20235
15 20235
16 20225
17 20214
18 20224
19 20184
20 20243

About Philipp Schillinger

Philipp Schillinger is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 24 papers that have together received 399 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (12 papers), Formal Methods in Verification (8 papers), Reinforcement Learning in Robotics (6 papers), Robotic Path Planning Algorithms (5 papers), AI-based Problem Solving and Planning (4 papers), Modular Robots and Swarm Intelligence (4 papers), Soft Robotics and Applications (3 papers) and Model-Driven Software Engineering Techniques (3 papers). The work is most often cited by research in Software (66 citations), Computational Theory and Mathematics (143 citations), Computer Vision and Pattern Recognition (142 citations), Artificial Intelligence (156 citations) and Control and Systems Engineering (95 citations). Philipp Schillinger has collaborated with scholars based in Germany, Sweden and United States. Frequent co-authors include Dimos V. Dimarogonas, Mathias Bürger, Oskar von Stryk, Stefan Kohlbrecher, David C. Conner, Hadas Kress‐Gazit, Vitchyr H. Pong, Patrizio Pelliccione, Davide Brugali and Daniel Strüber. Their work appears in journals such as The International Journal of Robotics Research, Robotics and Autonomous Systems, IEEE Robotics & Automation Magazine, Robotics and Computer-Integrated Manufacturing and Journal of Field Robotics.

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.

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