Matthew Chignoli

435 total citations
10 papers, 272 citations indexed

About

Matthew Chignoli is a scholar working on Biomedical Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matthew Chignoli has authored 10 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Biomedical Engineering, 5 papers in Control and Systems Engineering and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matthew Chignoli's work include Robotic Locomotion and Control (8 papers), Prosthetics and Rehabilitation Robotics (5 papers) and Robotic Mechanisms and Dynamics (3 papers). Matthew Chignoli is often cited by papers focused on Robotic Locomotion and Control (8 papers), Prosthetics and Rehabilitation Robotics (5 papers) and Robotic Mechanisms and Dynamics (3 papers). Matthew Chignoli collaborates with scholars based in United States. Matthew Chignoli's co-authors include Sangbae Kim, Patrick M. Wensing, Donghyun Kim, Yanran Ding, Gerardo Bledt, Seungwoo Hong and Jean-Jacques Slotine and has published in prestigious journals such as IEEE Access, IEEE Robotics and Automation Letters and 2022 International Conference on Robotics and Automation (ICRA).

In The Last Decade

Matthew Chignoli

8 papers receiving 258 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matthew Chignoli United States	8	214	118	51	47	34	10	272
Abhishek Pandala United States	7	237 1.1×	153 1.3×	64 1.3×	54 1.1×	39 1.1×	10	309
Wouter Wolfslag Netherlands	10	208 1.0×	139 1.2×	34 0.7×	46 1.0×	22 0.6×	23	272
Carlos Mastalli United Kingdom	8	240 1.1×	157 1.3×	79 1.5×	57 1.2×	49 1.4×	17	318
George Mesesan Germany	10	259 1.2×	129 1.1×	36 0.7×	37 0.8×	24 0.7×	20	310
Jan Carius Switzerland	6	217 1.0×	179 1.5×	88 1.7×	41 0.9×	34 1.0×	7	316
Seung‐Joon Yi South Korea	12	242 1.1×	137 1.2×	62 1.2×	60 1.3×	37 1.1×	42	335
R. Prasanth Kumar India	11	158 0.7×	200 1.7×	46 0.9×	59 1.3×	49 1.4×	38	365
Yukai Gong United States	5	210 1.0×	82 0.7×	34 0.7×	31 0.7×	29 0.9×	7	235
Andreea Radulescu United Kingdom	8	153 0.7×	77 0.7×	26 0.5×	30 0.6×	30 0.9×	11	198
Victor Klemm Switzerland	6	257 1.2×	169 1.4×	65 1.3×	72 1.5×	17 0.5×	11	320

Countries citing papers authored by Matthew Chignoli

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Chignoli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Chignoli

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

All Works

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10 of 10 papers shown

1.

Hong, Seungwoo, et al.. (2024). Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots. IEEE Robotics and Automation Letters. 9(12). 11074–11081. 15 indexed citations

2.

Chignoli, Matthew, et al.. (2024). Probabilistic Homotopy Optimization for Dynamic Motion Planning. 12856–12863.

3.

Chignoli, Matthew, Jean-Jacques Slotine, Patrick M. Wensing, & Sangbae Kim. (2024). URDF+: An Enhanced URDF for Robots with Kinematic Loops. 197–204.

4.

Ding, Yanran, et al.. (2023). Design and Development of the MIT Humanoid: A Dynamic and Robust Research Platform. 1–8. 10 indexed citations

5.

Ding, Yanran, et al.. (2022). Orientation-Aware Model Predictive Control with Footstep Adaptation for Dynamic Humanoid Walking. 299–305. 19 indexed citations

6.

Chignoli, Matthew, et al.. (2022). Rapid and Reliable Quadruped Motion Planning with Omnidirectional Jumping. 2022 International Conference on Robotics and Automation (ICRA). 6621–6627. 18 indexed citations

7.

Chignoli, Matthew & Sangbae Kim. (2021). Online Trajectory Optimization for Dynamic Aerial Motions of a Quadruped Robot. 7693–7699. 28 indexed citations

8.

Chignoli, Matthew, et al.. (2021). The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors. 1–8. 97 indexed citations

9.

Chignoli, Matthew & Patrick M. Wensing. (2020). Variational-Based Optimal Control of Underactuated Balancing for Dynamic Quadrupeds. IEEE Access. 8. 49785–49797. 56 indexed citations

10.

Chignoli, Matthew, et al.. (2020). Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments. 3664–3671. 29 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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