Matteo Leonetti

1.2k total citations
40 papers, 535 citations indexed

About

Matteo Leonetti is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matteo Leonetti has authored 40 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 13 papers in Control and Systems Engineering and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matteo Leonetti's work include Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (9 papers) and Robotic Path Planning Algorithms (6 papers). Matteo Leonetti is often cited by papers focused on Reinforcement Learning in Robotics (16 papers), Robot Manipulation and Learning (9 papers) and Robotic Path Planning Algorithms (6 papers). Matteo Leonetti collaborates with scholars based in United Kingdom, United States and Italy. Matteo Leonetti's 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 and has published in prestigious journals such as PLoS ONE, IEEE Access and Artificial Intelligence.

In The Last Decade

Matteo Leonetti

37 papers receiving 520 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matteo Leonetti United Kingdom 15 262 145 127 57 54 40 535
Francesco Setti Italy 13 172 0.7× 64 0.4× 314 2.5× 64 1.1× 88 1.6× 49 536
Lars Kunze United Kingdom 15 279 1.1× 216 1.5× 256 2.0× 51 0.9× 71 1.3× 64 676
Ransalu Senanayake United States 12 121 0.5× 93 0.6× 130 1.0× 28 0.5× 110 2.0× 37 423
Stefan Schiffer Germany 12 180 0.7× 112 0.8× 207 1.6× 60 1.1× 43 0.8× 52 608
Trevor Kistan Australia 15 83 0.3× 83 0.6× 56 0.4× 215 3.8× 84 1.6× 32 619
Curtis M. Humphrey United States 8 84 0.3× 83 0.6× 206 1.6× 144 2.5× 18 0.3× 16 531
Gennaro Notomista United States 13 111 0.4× 254 1.8× 169 1.3× 10 0.2× 82 1.5× 35 643
Ahmed Hussein United Kingdom 3 301 1.1× 219 1.5× 166 1.3× 19 0.3× 83 1.5× 4 618
Woong Kwon South Korea 11 56 0.2× 261 1.8× 96 0.8× 21 0.4× 64 1.2× 46 496

Countries citing papers authored by Matteo Leonetti

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Yan, Xuedong, et al.. (2025). Improving models of pedestrian crossing behavior using neural signatures of decision-making. Transportation Research Part F Traffic Psychology and Behaviour. 109. 1491–1506. 1 indexed citations
2.
Warburton, Matthew, Jack Brookes, Mohamed Hasan, et al.. (2024). Getting stuck in a rut as an emergent feature of a dynamic decision-making system. Royal Society Open Science. 11(4). 231550–231550. 1 indexed citations
3.
Sinapov, Jivko, Han Zhao, Muneeb Ahmad, et al.. (2024). Introduction to the Special Issue on Artificial Intelligence for Human–Robot Interaction (AI-HRI). ACM Transactions on Human-Robot Interaction. 13(3). 1–3.
4.
Canal, Gerard, et al.. (2024). Predicting When and What to Explain From Multimodal Eye Tracking and Task Signals. IEEE Transactions on Affective Computing. 16(1). 179–190. 2 indexed citations
5.
Leonetti, Matteo, et al.. (2024). Learning Social Cost Functions for Human-Aware Path Planning. 5364–5371. 2 indexed citations
6.
Markkula, Gustav, Yi-Shin Lin, Jac Billington, et al.. (2023). Explaining human interactions on the road by large-scale integration of computational psychological theory. PNAS Nexus. 2(6). pgad163–pgad163. 23 indexed citations
7.
Sun, Chengke, Anthony G. Cohn, & Matteo Leonetti. (2023). Online Human Capability Estimation Through Reinforcement Learning and Interaction. 7984–7991. 1 indexed citations
8.
Wei, Chongfeng, Yee Mun Lee, Christopher Holmes, et al.. (2023). Deceleration parameters as implicit communication signals for pedestrians’ crossing decisions and estimations of automated vehicle behaviour. Accident Analysis & Prevention. 190. 107173–107173. 35 indexed citations
9.
Attanasio, Aleks, Bruno Scaglioni, Alejandro F. Frangi, et al.. (2021). A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics. IEEE Transactions on Medical Robotics and Bionics. 3(1). 53–63. 10 indexed citations
10.
Narvekar, Sanmit, Bei Peng, Matteo Leonetti, et al.. (2020). Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. Journal of Machine Learning Research. 21(181). 1–50. 18 indexed citations
11.
Attanasio, Aleks, Bruno Scaglioni, Matteo Leonetti, et al.. (2020). Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery – A Feasibility Study. IEEE Robotics and Automation Letters. 5(4). 6528–6535. 46 indexed citations
12.
Raw, Rachael K., Richard M. Wilkie, Richard J. Allen, et al.. (2019). Skill acquisition as a function of age, hand and task difficulty: Interactions between cognition and action. PLoS ONE. 14(2). e0211706–e0211706. 18 indexed citations
13.
Leonetti, Matteo, et al.. (2018). Learning Deep Policies for Physics-Based Manipulation in Clutter.. arXiv (Cornell University). 1 indexed citations
14.
Narvekar, Sanmit, Jivko Sinapov, Matteo Leonetti, & Peter Stone. (2016). Source Task Creation for Curriculum Learning. Adaptive Agents and Multi-Agents Systems. 566–574. 36 indexed citations
15.
Leonetti, Matteo, et al.. (2016). State Aggregation through Reasoning in Answer Set Programming. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 1 indexed citations
16.
Sinapov, Jivko, Sanmit Narvekar, Matteo Leonetti, & Peter Stone. (2015). Learning Inter-Task Transferability in the Absence of Target Task Samples. Adaptive Agents and Multi-Agents Systems. 725–733. 17 indexed citations
17.
Yang, Fangkai, Piyush Khandelwal, Matteo Leonetti, & Peter Stone. (2014). Planning in answer set programming while learning action costs for mobile robots. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 71–78. 11 indexed citations
18.
Leonetti, Matteo, et al.. (2013). Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles. Spiral (Imperial College London). 1 indexed citations
19.
Leonetti, Matteo, S. Reza Ahmadzadeh, & Petar Kormushev. (2013). On-line learning to recover from thruster failures on Autonomous Underwater Vehicles. Spiral (Imperial College London). 1–6. 7 indexed citations
20.
Leonetti, Matteo & Luca Iocchi. (2010). Improving the performance of complex agent plans through reinforcement learning. Adaptive Agents and Multi-Agents Systems. 723–730. 2 indexed citations

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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