Leonard Hasenclever
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Biomedical Engineering
- Molecular Biology
- Co-authors
- Nicolas HeessJosh MerelYuval TassaGreg WayneArun AhujaVu PhamTom ErezSaran Tunyasuvunakool
- Topics
- Robot Manipulation and Learning (3 papers)Human Pose and Action Recognition (3 papers)Reinforcement Learning in Robotics (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionControl and Systems EngineeringComputer Graphics and Computer-Aided Design
- Partner nations
- United KingdomUnited StatesPoland
In The Last Decade
Leonard Hasenclever
9 papers receiving 150 citations
Peers
Comparison fields: 5 of 63
- Control and Systems Engineering 74
- Computer Vision and Pattern Recognition 73
- Artificial Intelligence 33
- Biomedical Engineering 26
- Molecular Biology 15
Countries citing papers authored by Leonard Hasenclever
This map shows the geographic impact of Leonard Hasenclever'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 Leonard Hasenclever with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonard Hasenclever more than expected).
Fields of papers citing papers by Leonard Hasenclever
This network shows the impact of papers produced by Leonard Hasenclever. 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 Leonard Hasenclever. The network helps show where Leonard Hasenclever may publish in the future.
Co-authorship network of co-authors of Leonard Hasenclever
This figure shows the co-authorship network connecting the top 25 collaborators of Leonard Hasenclever. A scholar is included among the top collaborators of Leonard Hasenclever 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 Leonard Hasenclever. Leonard Hasenclever is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 28 | |
| 3 | 12 | |
| 4 | CoMic: Complementary Task Learning & Mimicry for Reusable Skills | 5 |
| 5 | 59 | |
| 6 | 6 | |
| 7 | 4 | |
| 8 | Relativistic Monte Carlo | 5 |
| 9 | 14 |
About Leonard Hasenclever
Leonard Hasenclever is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 9 papers that have together received 153 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (3 papers), Human Pose and Action Recognition (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (73 citations), Control and Systems Engineering (74 citations) and Computer Graphics and Computer-Aided Design (6 citations). Leonard Hasenclever has collaborated with scholars based in United Kingdom, United States and Poland. Frequent co-authors include Nicolas Heess, Josh Merel, Yuval Tassa, Greg Wayne, Arun Ahuja, Vu Pham, Tom Erez, Saran Tunyasuvunakool, Steven Bohez and Arunkumar Byravan. Their work appears in journals such as Nature, ACM Transactions on Graphics and Natural Computing.
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.