Jürgen Leitner

58 papers receiving 1.1k citations

Jürgen Leitner's Hit Papers

Deep Learning Approaches to Grasp Synthesis: A Review 2023 · 114 citations
1140+2+5Years since publication100200300

Peers

Jürgen Leitner
Comparison fields: 5 of 96
  • Control and Systems Engineering 634
  • Human-Computer Interaction 123
  • Computer Vision and Pattern Recognition 382
  • Artificial Intelligence 376
  • Biomedical Engineering 341
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Countries citing papers authored by Jürgen Leitner

Since Specialization
Citations

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

Fields of papers citing papers by Jürgen Leitner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jürgen Leitner, 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 Jürgen Leitner Line = papers co-authored together Jürgen Leitner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 63 papers — load more, or switch the sort, to bring in the rest.

#Work
1
The limits and potentials of deep learning for robotics
Hit paper breakdown →
2018309
2
Learning robust, real-time, reactive robotic grasping
Hit paper breakdown →
2019296
3
Deep Learning Approaches to Grasp Synthesis: A Review
Hit paper breakdown →
2023114
4 201754
5 200945
6 201442
7 201929
8 201221
9 201921
10 201918
11 201214
12
Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks.
201713
13 202313
14 201813
15 201212
16 201311
17 201210
18 201210
19
Towards vision-based deep reinforcement learning for robotic motion control
20159
20 20139

About Jürgen Leitner

Jürgen Leitner is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering and Aerospace Engineering, having authored 63 papers that have together received 1.2k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (28 papers), Reinforcement Learning in Robotics (13 papers), Robotics and Sensor-Based Localization (11 papers), Robotic Locomotion and Control (10 papers), Advanced Vision and Imaging (9 papers), Human Pose and Action Recognition (7 papers), Modular Robots and Swarm Intelligence (7 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Control and Systems Engineering (634 citations), Human-Computer Interaction (123 citations), Computer Vision and Pattern Recognition (382 citations), Artificial Intelligence (376 citations) and Biomedical Engineering (341 citations). Jürgen Leitner has collaborated with scholars based in Australia, Switzerland and United Kingdom. Frequent co-authors include Peter Corke, Douglas Morrison, Michael Milford, Dieter Fox, Ben Upcroft, Jürgen Schmidhuber, Niko Sünderhauf, Wolfram Burgard, Oliver Brock and Pieter Abbeel. Their work appears in journals such as Nature Machine Intelligence, The International Journal of Robotics Research, IEEE Robotics and Automation Letters, IEEE Transactions on Robotics and International Journal of Computer Vision.

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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