Rogerio Bonatti
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Aerospace Engineering
- Industrial and Manufacturing Engineering
- Co-authors
- Arthur BuckerAshish KapoorSai VempralaShuang MaLuis FigueredoSebastian SchererGuilherme A. S. PereiraSami Haddadin
- Topics
- Multimodal Machine Learning Applications (4 papers)Advanced Vision and Imaging (3 papers)Topic Modeling (3 papers)
- Journals
- IEEE AccessSensors2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Rogerio Bonatti
12 papers receiving 320 citations
Hit Papers
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 149
- Artificial Intelligence 147
- Control and Systems Engineering 89
- Aerospace Engineering 54
- Industrial and Manufacturing Engineering 17
Countries citing papers authored by Rogerio Bonatti
This map shows the geographic impact of Rogerio Bonatti'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 Rogerio Bonatti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rogerio Bonatti more than expected).
Fields of papers citing papers by Rogerio Bonatti
This network shows the impact of papers produced by Rogerio Bonatti. 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 Rogerio Bonatti. The network helps show where Rogerio Bonatti may publish in the future.
Co-authorship network of co-authors of Rogerio Bonatti
This figure shows the co-authorship network connecting the top 25 collaborators of Rogerio Bonatti. A scholar is included among the top collaborators of Rogerio Bonatti 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 Rogerio Bonatti. Rogerio Bonatti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | ChatGPT for Robotics: Design Principles and Model Abilitiesbreakdown → | 198 |
| 3 | 5 | |
| 4 | 26 | |
| 5 | 12 | |
| 6 | 2 | |
| 7 | 37 | |
| 8 | 28 | |
| 9 | 11 | |
| 10 | Learning Controls Using Cross-Modal Representations: Bridging Simulation and Reality for Drone Racing. | 2 |
| 11 | 2 | |
| 12 | Effect of Part-of-Speech and Lemmatization Filtering in Email Classification for Automatic Reply. | 3 |
About Rogerio Bonatti
Rogerio Bonatti is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 12 papers that have together received 327 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Advanced Vision and Imaging (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (149 citations), Health Informatics (7 citations) and Artificial Intelligence (147 citations). Rogerio Bonatti has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Arthur Bucker, Ashish Kapoor, Sai Vemprala, Shuang Ma, Luis Figueredo, Sebastian Scherer, Guilherme A. S. Pereira, Ashish Kapoor, Sami Haddadin and Andrew Jong. Their work appears in journals such as IEEE Access, Sensors and 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.