Matt Zucker
- Computer Vision and Pattern Recognition top 0.5%
- Control and Systems Engineering top 1%
- Aerospace Engineering top 2%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Co-authors
- J. Andrew BagnellNathan RatliffSiddhartha S SrinivasaJames KuffnerMichael S. BranickyAnca D. DraganMihail PivtoraikoMatthew Klingensmith
- Topics
- Robotic Path Planning Algorithms (9 papers)Robotic Locomotion and Control (6 papers)Robot Manipulation and Learning (5 papers)
- Cited by
- Computer Vision and Pattern RecognitionControl and Systems EngineeringAerospace Engineering
- Journals
- The International Journal of Robotics ResearchIEEE Robotics & Automation MagazineJournal of Field Robotics
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Matt Zucker
15 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 1.2k
- Control and Systems Engineering 878
- Aerospace Engineering 588
- Biomedical Engineering 359
- Artificial Intelligence 267
Countries citing papers authored by Matt Zucker
This map shows the geographic impact of Matt Zucker'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 Matt Zucker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt Zucker more than expected).
Fields of papers citing papers by Matt Zucker
This network shows the impact of papers produced by Matt Zucker. 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 Matt Zucker. The network helps show where Matt Zucker may publish in the future.
Co-authorship network of co-authors of Matt Zucker
This figure shows the co-authorship network connecting the top 25 collaborators of Matt Zucker. A scholar is included among the top collaborators of Matt Zucker 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 Matt Zucker. Matt Zucker 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 | 51 | |
| 3 | 3 | |
| 4 | 20 | |
| 5 | CHOMP: Covariant Hamiltonian optimization for motion planningbreakdown → | 470 |
| 6 | 11 | |
| 7 | 10 | |
| 8 | 92 | |
| 9 | 53 | |
| 10 | CHOMP: Gradient optimization techniques for efficient motion planningbreakdown → | 650 |
| 11 | 54 | |
| 12 | 205 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 11 |
About Matt Zucker
Matt Zucker is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Biomedical Engineering, having authored 15 papers that have together received 1.6k indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (9 papers), Robotic Locomotion and Control (6 papers) and Robot Manipulation and Learning (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Control and Systems Engineering (878 citations) and Aerospace Engineering (588 citations). Matt Zucker has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include J. Andrew Bagnell, Nathan Ratliff, Siddhartha S Srinivasa, James Kuffner, Michael S. Branicky, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin and Christopher G. Atkeson. Their work appears in journals such as The International Journal of Robotics Research, IEEE Robotics & Automation Magazine and Journal of Field Robotics.
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.