Petar Kormushev

2.7k total citations
98 papers, 1.8k citations indexed

About

Petar Kormushev is a scholar working on Control and Systems Engineering, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Petar Kormushev has authored 98 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Control and Systems Engineering, 48 papers in Biomedical Engineering and 35 papers in Artificial Intelligence. Recurrent topics in Petar Kormushev's work include Robot Manipulation and Learning (42 papers), Reinforcement Learning in Robotics (25 papers) and Robotic Locomotion and Control (22 papers). Petar Kormushev is often cited by papers focused on Robot Manipulation and Learning (42 papers), Reinforcement Learning in Robotics (25 papers) and Robotic Locomotion and Control (22 papers). Petar Kormushev collaborates with scholars based in Italy, United Kingdom and Spain. Petar Kormushev's co-authors include Darwin G. Caldwell, Sylvain Calinon, Fabio Pardo, S. Reza Ahmadzadeh, Marc Carreras, Nikos G. Tsagarakis, Weibang Bai, Barkan Uğurlu, Kostas J. Kyriakopoulos and Eric M. Yeatman and has published in prestigious journals such as IEEE Access, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Robotics.

In The Last Decade

Petar Kormushev

96 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Petar Kormushev Italy 19 1.0k 697 547 286 282 98 1.8k
Yunyi Jia United States 22 752 0.7× 306 0.4× 314 0.6× 271 0.9× 426 1.5× 145 2.0k
Samia Nefti‐Meziani United Kingdom 22 542 0.5× 1.0k 1.5× 288 0.5× 338 1.2× 240 0.9× 80 1.9k
Takamitsu Matsubara Japan 23 896 0.9× 1000 1.4× 518 0.9× 221 0.8× 274 1.0× 150 2.0k
Ross A. Knepper United States 25 666 0.7× 321 0.5× 525 1.0× 244 0.9× 948 3.4× 44 2.0k
Eric Rohmer Brazil 12 568 0.6× 562 0.8× 248 0.5× 290 1.0× 437 1.5× 55 1.4k
Luca Bascetta Italy 23 935 0.9× 321 0.5× 171 0.3× 338 1.2× 333 1.2× 106 1.5k
Di‐Hua Zhai China 25 1.2k 1.2× 232 0.3× 227 0.4× 441 1.5× 187 0.7× 102 1.8k
Brett Browning United States 18 1.5k 1.5× 395 0.6× 1.3k 2.3× 319 1.1× 879 3.1× 53 2.8k
Jie Tan United States 22 916 0.9× 532 0.8× 764 1.4× 148 0.5× 573 2.0× 53 2.2k
Weiwei Wan Japan 24 1.1k 1.1× 558 0.8× 177 0.3× 354 1.2× 596 2.1× 173 1.7k

Countries citing papers authored by Petar Kormushev

Since Specialization
Citations

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

Fields of papers citing papers by Petar Kormushev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Petar Kormushev

This figure shows the co-authorship network connecting the top 25 collaborators of Petar Kormushev. A scholar is included among the top collaborators of Petar Kormushev 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 Petar Kormushev. Petar Kormushev 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.
2.
Wang, Ke, et al.. (2023). A unified model with inertia shaping for highly dynamic jumps of legged robots. Mechatronics. 95. 103040–103040. 6 indexed citations
3.
Lanari, Leonardo, et al.. (2021). Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization. IEEE Robotics and Automation Letters. 6(2). 2642–2649. 17 indexed citations
4.
Wang, Ke, et al.. (2020). Design and Control of SLIDER: An Ultra-lightweight, Knee-less, Low-cost Bipedal Walking Robot. Spiral (Imperial College London). 3488–3495. 14 indexed citations
5.
Kormushev, Petar, et al.. (2019). Active learning via informed search in movement parameter space for efficient robot task learning and transfer. Autonomous Robots. 43(8). 1917–1935. 7 indexed citations
6.
Kormushev, Petar, et al.. (2018). Casualty detection for mobile rescue robots via ground-projected point clouds. Spiral (Imperial College London). 1 indexed citations
7.
Caldwell, Darwin G., et al.. (2017). Climbing over large obstacles with a humanoid robot via multi-contact motion planning. Spiral (Imperial College London). 5 indexed citations
8.
Maurelli, Francesco, Marc Carreras, Joaquím Salví, et al.. (2016). The PANDORA project: A success story in AUV autonomy. OCEANS 2016 - Shanghai. 1–8. 25 indexed citations
9.
Ahmadzadeh, S. Reza, et al.. (2015). Learning symbolic representations of actions from human demonstrations. CINECA IRIS Institutial Research Information System (University of Genoa). 3801–3808. 44 indexed citations
10.
Jamisola, Rodrigo S., Petar Kormushev, Darwin G. Caldwell, & F. Ibikunle. (2015). Modular relative Jacobian for dual-arms and the wrench transformation matrix. Landmark University Repository (Landmark University). 2. 181–186. 11 indexed citations
11.
Kormushev, Petar & Darwin G. Caldwell. (2013). Comparative Evaluation of Reinforcement Learning with Scalar Rewards and Linear Regression with Multidimensional Feedback. Spiral (Imperial College London). 3 indexed citations
12.
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
13.
Leonetti, Matteo, et al.. (2013). Online Direct Policy Search for Thruster Failure Recovery in Autonomous Underwater Vehicles. Spiral (Imperial College London). 1 indexed citations
14.
Kormushev, Petar & Darwin G. Caldwell. (2013). Towards improved AUV control through learning of periodic signals. Spiral (Imperial College London). 1–4. 5 indexed citations
15.
Karras, George C., Charalampos P. Bechlioulis, Matteo Leonetti, et al.. (2013). On-line identification of autonomous underwater vehicles through global derivative-free optimization. Spiral (Imperial College London). 3859–3864. 25 indexed citations
16.
Kormushev, Petar & Darwin G. Caldwell. (2012). Direct policy search reinforcement learning based on particle filtering. Spiral (Imperial College London). 8 indexed citations
17.
Kormushev, Petar, Sylvain Calinon, & Darwin G. Caldwell. (2010). Approaches for Learning Human-like Motor Skills which Require Variable Stiffness During Execution. Spiral (Imperial College London). 2 indexed citations
18.
Kormushev, Petar, Sylvain Calinon, & Darwin G. Caldwell. (2010). Robot motor skill coordination with EM-based Reinforcement Learning. Spiral (Imperial College London). 3232–3237. 180 indexed citations
19.
Kormushev, Petar, et al.. (2009). Time manipulation technique for speeding up reinforcement learning in simulations. ArXiv.org. 2 indexed citations
20.
Agre, Gennady, et al.. (2006). INFRAWEBS Axiom Editor - A graphical ontology-driven tool for creating complex logical expressions. Bulgarian Digital Mathematics Library (BulDML) at IMI-BAS (Institute of Mathematics and Informatics). 13. 1 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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