Michael Lutter

403 total citations
10 papers, 98 citations indexed

About

Michael Lutter is a scholar working on Control and Systems Engineering, Artificial Intelligence and Social Psychology. According to data from OpenAlex, Michael Lutter has authored 10 papers receiving a total of 98 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Control and Systems Engineering, 5 papers in Artificial Intelligence and 2 papers in Social Psychology. Recurrent topics in Michael Lutter's work include Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (4 papers) and Real-time simulation and control systems (2 papers). Michael Lutter is often cited by papers focused on Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (4 papers) and Real-time simulation and control systems (2 papers). Michael Lutter collaborates with scholars based in Germany, Israel and Canada. Michael Lutter's co-authors include Jan Peters, Christian Ritter, Dorothea Koert, Marco Ewerton, Debora Clever, Stephan Rinderknecht, Animesh Garg, Dieter Fox, Shie Mannor and Kim D. Listmann and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The International Journal of Robotics Research and International Journal of Humanoid Robotics.

In The Last Decade

Michael Lutter

9 papers receiving 96 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Lutter Germany	5	51	33	27	23	19	10	98
Anthony L. Caterini United Kingdom	2	18 0.4×	34 1.0×	6 0.2×	6 0.3×	11 0.6×	3	80
Juan Carlos Travieso‐Torres Chile	11	231 4.5×	13 0.4×	13 0.5×	18 0.8×	4 0.2×	42	301
Guillaume Moroz France	6	57 1.1×	4 0.1×	4 0.1×	11 0.5×	26 1.4×	9	95
I. Koštial Slovakia	4	222 4.4×	13 0.4×	10 0.4×	20 0.9×	10 0.5×	8	252
Monimoy Bujarbaruah United States	7	145 2.8×	22 0.7×	2 0.1×	12 0.5×	6 0.3×	11	188
Jianbin Qiu China	3	65 1.3×	12 0.4×	4 0.1×	6 0.3×	2 0.1×	7	101
Samer Riachy France	7	216 4.2×	6 0.2×	7 0.3×	50 2.2×	20 1.1×	20	246
Andrew P. Featherstone United States	7	225 4.4×	7 0.2×	7 0.3×	34 1.5×	9 0.5×	9	267
Sylvain Koos France	2	38 0.7×	101 3.1×	2 0.1×	65 2.8×	31 1.6×	3	155
I. G. Mamedov	2	241 4.7×	22 0.7×	13 0.5×	38 1.7×	4 0.2×	2	273

Countries citing papers authored by Michael Lutter

Since Specialization

Citations

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

Fields of papers citing papers by Michael Lutter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Lutter

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

All Works

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10 of 10 papers shown

1.

Lutter, Michael, Christian Ritter, & Jan Peters. (2023). Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. TUbilio (Technical University of Darmstadt). 43 indexed citations

2.

Lutter, Michael & Jan Peters. (2023). Combining physics and deep learning to learn continuous-time dynamics models. The International Journal of Robotics Research. 42(3). 83–107. 26 indexed citations

3.

Lutter, Michael, et al.. (2022). Continuous-Time Fitted Value Iteration for Robust Policies. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1–15. 5 indexed citations

4.

Clever, Debora, et al.. (2021). Trajectory Optimization of Energy Consumption and Expected Service Life of a Robotic System. TUbilio (Technical University of Darmstadt). 842–847. 3 indexed citations

5.

Lutter, Michael, et al.. (2021). Building Skill Learning Systems for Robotics. 17. 1878–1883. 4 indexed citations

6.

Lutter, Michael, Shie Mannor, Jan Peters, Dieter Fox, & Animesh Garg. (2021). Value Iteration in Continuous Actions, States and Time. 7224–7234. 1 indexed citations

7.

Lutter, Michael & Jan Peters. (2020). Differential Equations as a Model Prior for Deep Learning and its Applications in Robotics. International Conference on Learning Representations.

8.

Lutter, Michael, et al.. (2019). HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints.. arXiv (Cornell University). 640–650. 1 indexed citations

9.

Koert, Dorothea, et al.. (2019). Incremental Learning of an Open-Ended Collaborative Skill Library. International Journal of Humanoid Robotics. 17(1). 2050001–2050001. 4 indexed citations

10.

Koert, Dorothea, et al.. (2018). Online Learning of an Open-Ended Skill Library for Collaborative Tasks. Technischen Universität Darmstadt. 1–9. 11 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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