Roland Hafner

2.2k total citations
15 papers, 410 citations indexed

About

Roland Hafner is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Roland Hafner has authored 15 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 6 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Roland Hafner's work include Reinforcement Learning in Robotics (10 papers), Robot Manipulation and Learning (3 papers) and Advanced Control Systems Optimization (2 papers). Roland Hafner is often cited by papers focused on Reinforcement Learning in Robotics (10 papers), Robot Manipulation and Learning (3 papers) and Advanced Control Systems Optimization (2 papers). Roland Hafner collaborates with scholars based in Germany, United States and United Kingdom. Roland Hafner's co-authors include Martin Riedmiller, Sascha Lange, Thomas Gabel, Thomas Lampe, Michael Neunert, Nicolas Heess, Jost Tobias Springenberg, Jonas Degrave, Volodymyr Mnih and Tom Van de Wiele and has published in prestigious journals such as Machine Learning, Autonomous Robots and Journal of Applied Probability.

In The Last Decade

Roland Hafner

12 papers receiving 375 citations

Peers

Roland Hafner
Roland Hafner
Citations per year, relative to Roland Hafner Roland Hafner (= 1×) peers Tuomas Haarnoja

Countries citing papers authored by Roland Hafner

Since Specialization
Citations

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

Fields of papers citing papers by Roland Hafner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roland Hafner

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

All Works

15 of 15 papers shown
1.
Bauzá, Maria, Jingyi Chen, Valentin Dalibard, et al.. (2025). DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots. 6756–6763.
2.
Lampe, Thomas, Abbas Abdolmaleki, Sandy H. Huang, et al.. (2024). Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. 5. 7772–7779. 1 indexed citations
3.
Wulfmeier, Markus, Dushyant Rao, Roland Hafner, et al.. (2021). Data-efficient Hindsight Off-policy Option Learning. arXiv (Cornell University). 11340–11350. 3 indexed citations
4.
Bloesch, Michael, Jan Humplik, Viorica Pătrăucean, et al.. (2021). Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. 2 indexed citations
5.
Siegel, Noah, Jost Tobias Springenberg, Felix Berkenkamp, et al.. (2020). Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. arXiv (Cornell University). 17 indexed citations
6.
Schwab, Devin, Jost Tobias Springenberg, Thomas Lampe, et al.. (2019). Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup. arXiv (Cornell University). 15. 2 indexed citations
7.
Riedmiller, Martin, Roland Hafner, Thomas Lampe, et al.. (2018). Learning by Playing - Solving Sparse Reward Tasks from Scratch. International Conference on Machine Learning. 4344–4353. 48 indexed citations
8.
Hafner, Roland & Martin Riedmiller. (2011). Reinforcement learning in feedback control. Machine Learning. 84(1-2). 137–169. 132 indexed citations
9.
Lauer, Martin, Roland Hafner, Sascha Lange, & Martin Riedmiller. (2010). Cognitive concepts in autonomous soccer playing robots. Cognitive Systems Research. 11(3). 287–309. 5 indexed citations
10.
Riedmiller, Martin, Thomas Gabel, Roland Hafner, & Sascha Lange. (2009). Reinforcement learning for robot soccer. Autonomous Robots. 27(1). 55–73. 160 indexed citations
11.
Riedmiller, Martin, Roland Hafner, Sascha Lange, & Martin Lauer. (2008). Learning to dribble on a real robot by success and failure. 2. 2207–2208. 3 indexed citations
12.
Hafner, Roland & Martin Riedmiller. (2007). Neural Reinforcement Learning Controllers for a Real Robot Application. Proceedings - IEEE International Conference on Robotics and Automation/Proceedings. 6. 2098–2103. 34 indexed citations
13.
Riedmiller, Martin, Thomas Gabel, Roland Hafner, Sascha Lange, & Martin Lauer. (2006). Die Brainstormers: Entwurfsprinzipien lernfähiger autonomer Roboter. Informatik-Spektrum. 29(3). 175–190. 3 indexed citations
14.
Hafner, Roland, et al.. (1974). Some asymptotic results on multiple matching. Journal of Applied Probability. 11(3). 479–492.
15.
Hafner, Roland, et al.. (1974). Some asymptotic results on multiple matching. Journal of Applied Probability. 11(3). 479–492.

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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