Matthias Kerzel

716 total citations
43 papers, 380 citations indexed

About

Matthias Kerzel is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Matthias Kerzel has authored 43 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 14 papers in Cognitive Neuroscience. Recurrent topics in Matthias Kerzel's work include Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (8 papers) and Human Pose and Action Recognition (7 papers). Matthias Kerzel is often cited by papers focused on Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (8 papers) and Human Pose and Action Recognition (7 papers). Matthias Kerzel collaborates with scholars based in Germany, China and Netherlands. Matthias Kerzel's co-authors include Stefan Wermter, Erik Strahl, Manfred Eppe, Stefan Heinrich, Cornelius Weber, Martin V. Butz, Chu Kiong Loo, Sven Magg, Nicolás Navarro-Guerrero and Pablo Barros and has published in prestigious journals such as IEEE Access, ACM Computing Surveys and Neurocomputing.

In The Last Decade

Matthias Kerzel

42 papers receiving 370 citations

Peers

Matthias Kerzel
Nicolás Navarro-Guerrero Germany
Sven Magg Germany
Alessandro Roncone United States
Phillip Walker United States
Martim Brandão United Kingdom
Essam Debie Australia
Massimiliano Patacchiola United Kingdom
Leon Bodenhagen Denmark
Noriaki Mitsunaga Japan
Francesco Semeraro Italy
Nicolás Navarro-Guerrero Germany
Matthias Kerzel
Citations per year, relative to Matthias Kerzel Matthias Kerzel (= 1×) peers Nicolás Navarro-Guerrero

Countries citing papers authored by Matthias Kerzel

Since Specialization
Citations

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

Fields of papers citing papers by Matthias Kerzel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthias Kerzel

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Kerzel. A scholar is included among the top collaborators of Matthias Kerzel 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 Matthias Kerzel. Matthias Kerzel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kerzel, Matthias, et al.. (2024). Robotic Imitation of Human Actions. 1–6. 1 indexed citations
2.
Fu, Di, Matthias Kerzel, Ziwei Chen, et al.. (2023). A Trained Humanoid Robot can Perform Human-Like Crossmodal Social Attention and Conflict Resolution. International Journal of Social Robotics. 15(8). 1325–1340. 8 indexed citations
3.
4.
Kerzel, Matthias, et al.. (2022). Language-Model-Based Paired Variational Autoencoders for Robotic Language Learning. IEEE Transactions on Cognitive and Developmental Systems. 15(4). 1812–1824. 3 indexed citations
5.
Kerzel, Matthias, et al.. (2022). Self-organized Learning from Synthetic and Real-World Data for a Humanoid Exercise Robot. Frontiers in Robotics and AI. 9. 669719–669719. 1 indexed citations
6.
Kerzel, Matthias, et al.. (2022). Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks. 121. 217–222. 1 indexed citations
7.
Kerzel, Matthias, et al.. (2021). Learning Then, Learning Now, and Every Second in Between: Lifelong Learning With a Simulated Humanoid Robot. Frontiers in Neurorobotics. 15. 669534–669534. 6 indexed citations
8.
Weber, Cornelius, et al.. (2020). Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination. Robotics and Autonomous Systems. 133. 103630–103630. 11 indexed citations
9.
Loo, Chu Kiong, et al.. (2020). Explainable Goal-Driven Agents and Robots -- A Comprehensive Review and New Framework. arXiv (Cornell University). 7 indexed citations
10.
Kerzel, Matthias, et al.. (2020). Teaching NICO How to Grasp: An Empirical Study on Crossmodal Social Interaction as a Key Factor for Robots Learning From Humans. Frontiers in Neurorobotics. 14. 28–28. 13 indexed citations
11.
Fu, Di, Cornelius Weber, Guochun Yang, et al.. (2020). What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective. Frontiers in Integrative Neuroscience. 14. 10–10. 17 indexed citations
12.
Kerzel, Matthias, et al.. (2020). Enhancing a Neurocognitive Shared Visuomotor Model for Object Identification, Localization, and Grasping With Learning From Auxiliary Tasks. IEEE Transactions on Cognitive and Developmental Systems. 14(4). 1331–1343. 3 indexed citations
13.
Barakova, Emilia, et al.. (2020). Improving Emotional Expression Recognition of Robots Using Regions of Interest from Human Data. TU/e Research Portal. 142–144. 1 indexed citations
14.
Fu, Di, Cornelius Weber, Guochun Yang, et al.. (2019). What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective. PsyArXiv (OSF Preprints). 1 indexed citations
15.
Fu, Di, Cornelius Weber, Matthias Kerzel, et al.. (2019). What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective. PsyArXiv (OSF Preprints). 1 indexed citations
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Kerzel, Matthias, et al.. (2018). Slowness-based neural visuomotor control with an Intrinsically motivated Continuous Actor-Critic.. ePrints Soton (University of Southampton). 1 indexed citations
18.
Kerzel, Matthias, Erik Strahl, Sven Magg, et al.. (2017). NICO — Neuro-inspired companion: A developmental humanoid robot platform for multimodal interaction. 113–120. 47 indexed citations
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20.
Habel, Christopher, et al.. (2010). Verbal assistance in tactile-map explorations: a case for visual representations and reasoning. National Conference on Artificial Intelligence. 34–41. 9 indexed citations

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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