Robin Strudel

1.3k citations
5 papers · 48 indexed · h-index 3
Topics
Reinforcement Learning in Robotics (3 papers)Robot Manipulation and Learning (2 papers)Multimodal Machine Learning Applications (2 papers)
Journals
2021 IEEE/CVF International Conference on Computer Vision (ICCV)HAL (Le Centre pour la Communication Scientifique Directe)arXiv (Cornell University)
Partner nations
FranceUnited States

In The Last Decade

Robin Strudel

5 papers receiving 43 citations

Peers

Robin Strudel
Comparison fields: 5 of 29
  • Computer Vision and Pattern Recognition 24
  • Artificial Intelligence 22
  • Control and Systems Engineering 15
  • Industrial and Manufacturing Engineering 6
  • Computational Mechanics 4
Replace Gautam Salhotra with:
Gautam Salhotra United States
Ajay Jain United States
Zhuo Xu United States
Damian Mrowca United States
Nur Muhammad Mahi Shafiullah United States
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Karl Pertsch United States
Filippos Christianos United Kingdom
Huaxia Xia China
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Robin Strudel relative to Gautam Salhotra United States Gautam Salhotra's profile →
Citations per field
00.5×1.5×
Gautam Salhotra · 1×
Citations per year

Countries citing papers authored by Robin Strudel

Since Specialization
Citations

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

Fields of papers citing papers by Robin Strudel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robin Strudel

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

All Works

5 of 5 papers shown
#WorkIndexed citations
1 2
2 2
3 11
4
Combining learned skills and reinforcement learning for robotic manipulations.
2
5 31

About Robin Strudel

Robin Strudel is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Artificial Intelligence, having authored 5 papers that have together received 48 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Robot Manipulation and Learning (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (24 citations), Health Informatics (1 citation) and Artificial Intelligence (22 citations). Robin Strudel has collaborated with scholars based in France and United States. Frequent co-authors include Ivan Laptev, Cordelia Schmid, Ricardo Garcı́a, Shizhe Chen, Josef Šivic and Jean Ponce. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).

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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