Matteo Leonetti
- Artificial Intelligence top 5%
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Social Psychology
- Automotive Engineering
- Co-authors
- Peter StoneJivko SinapovLuca IocchiSanmit NarvekarPetar KormushevPiyush KhandelwalFangkai YangMehmet R. Doğar
- Topics
- Reinforcement Learning in Robotics (16 papers)Robot Manipulation and Learning (9 papers)Robotic Path Planning Algorithms (6 papers)
- Cited by
- Artificial IntelligenceControl and Systems EngineeringComputer Vision and Pattern Recognition
- Partner nations
- United KingdomUnited StatesItaly
In The Last Decade
Matteo Leonetti
37 papers receiving 520 citations
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 262
- Control and Systems Engineering 145
- Computer Vision and Pattern Recognition 127
- Social Psychology 57
- Automotive Engineering 54
Countries citing papers authored by Matteo Leonetti
This map shows the geographic impact of Matteo Leonetti'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 Matteo Leonetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Leonetti more than expected).
Fields of papers citing papers by Matteo Leonetti
This network shows the impact of papers produced by Matteo Leonetti. 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 Matteo Leonetti. The network helps show where Matteo Leonetti may publish in the future.
Co-authorship network of co-authors of Matteo Leonetti
This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Leonetti. A scholar is included among the top collaborators of Matteo Leonetti 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 Matteo Leonetti. Matteo Leonetti 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 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 23 | |
| 7 | 1 | |
| 8 | 35 | |
| 9 | 10 | |
| 10 | Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey | 18 |
| 11 | 46 | |
| 12 | 18 | |
| 13 | Learning Deep Policies for Physics-Based Manipulation in Clutter. | 1 |
| 14 | 36 | |
| 15 | State Aggregation through Reasoning in Answer Set Programming | 1 |
| 16 | 17 | |
| 17 | Planning in answer set programming while learning action costs for mobile robots | 11 |
| 18 | Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles | 1 |
| 19 | On-line learning to recover from thruster failures on Autonomous Underwater Vehicles | 7 |
| 20 | 2 |
About Matteo Leonetti
Matteo Leonetti is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 40 papers that have together received 535 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (9 papers) and Robotic Path Planning Algorithms (6 papers). The work is most often cited by research in Artificial Intelligence (262 citations), Control and Systems Engineering (145 citations) and Computer Vision and Pattern Recognition (127 citations). Matteo Leonetti has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include Peter Stone, Jivko Sinapov, Luca Iocchi, Sanmit Narvekar, Petar Kormushev, Piyush Khandelwal, Fangkai Yang, Mehmet R. Doğar, Alejandro F. Frangi and Pietro Valdastri. Their work appears in journals such as PLoS ONE, IEEE Access and Artificial Intelligence.
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.