Thomas Kollar

3.1k total citations · 1 hit paper
45 papers, 1.6k citations indexed

About

Thomas Kollar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Thomas Kollar has authored 45 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 12 papers in Control and Systems Engineering. Recurrent topics in Thomas Kollar's work include Multimodal Machine Learning Applications (14 papers), Robotics and Sensor-Based Localization (12 papers) and Natural Language Processing Techniques (12 papers). Thomas Kollar is often cited by papers focused on Multimodal Machine Learning Applications (14 papers), Robotics and Sensor-Based Localization (12 papers) and Natural Language Processing Techniques (12 papers). Thomas Kollar collaborates with scholars based in United States, Chile and Italy. Thomas Kollar's co-authors include Nicholas Roy, Stefanie Tellex, Seth Teller, Nicholas Roy, Nicholas Roy, Matthew R. Walter, Ashis G. Banerjee, Jayant Krishnamurthy, Deb Roy and Emma Brunskill and has published in prestigious journals such as The International Journal of Robotics Research, Robotics and Autonomous Systems and IEEE Robotics and Automation Letters.

In The Last Decade

Thomas Kollar

42 papers receiving 1.5k citations

Hit Papers

Understanding Natural Language Commands for Robotic Navig... 2011 2026 2016 2021 2011 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
    2011 368 citations Stefanie Tellex, Thomas Kollar et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Kollar United States 23 869 860 401 358 146 45 1.6k
Christoph Sprunk Germany 13 776 0.9× 239 0.3× 326 0.8× 412 1.2× 98 0.7× 18 1.1k
David Kortenkamp United States 20 665 0.8× 624 0.7× 376 0.9× 549 1.5× 201 1.4× 82 1.7k
Monica Nicolescu United States 19 653 0.8× 612 0.7× 542 1.4× 101 0.3× 230 1.6× 102 1.4k
Lei Tai Hong Kong 12 1.0k 1.2× 582 0.7× 239 0.6× 488 1.4× 34 0.2× 20 1.4k
Stefanie Tellex United States 26 1.1k 1.2× 1.5k 1.7× 620 1.5× 206 0.6× 339 2.3× 85 2.3k
Joydeep Biswas United States 17 692 0.8× 360 0.4× 217 0.5× 510 1.4× 141 1.0× 77 1.4k
Anne Spalanzani France 16 680 0.8× 239 0.3× 261 0.7× 230 0.6× 345 2.4× 51 1.1k
R. Peter Bonasso United States 13 386 0.4× 466 0.5× 247 0.6× 202 0.6× 119 0.8× 49 987
Nick Hawes United Kingdom 21 419 0.5× 610 0.7× 234 0.6× 168 0.5× 121 0.8× 103 1.2k
Ramón Barber Spain 16 402 0.5× 204 0.2× 267 0.7× 274 0.8× 144 1.0× 79 780

Countries citing papers authored by Thomas Kollar

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Kollar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Kollar

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Kollar. A scholar is included among the top collaborators of Thomas Kollar 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 Thomas Kollar. Thomas Kollar 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.
Schwager, Mac, et al.. (2024). How Generalizable is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation. IEEE Robotics and Automation Letters. 9(10). 8619–8626.
2.
Chi, Hyung‐gun, et al.. (2024). Multi-Modal Representation Learning with Tactile Data. 9660–9667. 2 indexed citations
3.
Cheng, Richard K., et al.. (2024). MANIP: A Modular Architecture for Integrating Interactive Perception for Robot Manipulation. eScholarship (California Digital Library). 1283–1289.
4.
Finn, Chelsea, et al.. (2024). A Critical Evaluation of AI Feedback for Aligning Large Language Models. 29166–29190.
5.
Karamcheti, Siddharth, Suraj Nair, Annie Chen, et al.. (2023). Language-Driven Representation Learning for Robotics. 34 indexed citations
6.
Seita, Daniel, et al.. (2023). Bagging by Learning to Singulate Layers Using Interactive Perception. 3176–3183. 2 indexed citations
7.
Kollar, Thomas, et al.. (2022). CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation. 2022 International Conference on Robotics and Automation (ICRA). 10632–10640. 43 indexed citations
8.
Kollar, Thomas, et al.. (2021). Using the Web to Interactively Learn to Find Objects. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 2074–2080. 3 indexed citations
9.
Deits, Robin, et al.. (2013). Clarifying Commands with Information-Theoretic Human-Robot Dialog. 2(2). 42 indexed citations
10.
Kollar, Thomas, et al.. (2013). Learning environmental knowledge from task-based human-robot dialog. IRIS Research product catalog (Sapienza University of Rome). 8. 4304–4309. 40 indexed citations
11.
Kollar, Thomas, et al.. (2013). Indoor scene recognition by a mobile robot through adaptive object detection. Robotics and Autonomous Systems. 61(9). 932–947. 44 indexed citations
12.
Kollar, Thomas, et al.. (2012). Enabling robots to find and fetch objects by querying the web. Adaptive Agents and Multi-Agents Systems. 1217–1218. 5 indexed citations
13.
Kollar, Thomas, Stefanie Tellex, Deb Roy, & Nicholas Roy. (2012). Grounding Verbs of Motion in Natural Language Commands to Robots. DSpace@MIT (Massachusetts Institute of Technology). 22 indexed citations
14.
Kollar, Thomas, Stefanie Tellex, & Nicholas Roy. (2010). A discriminative model for understanding natural language route directions. DSpace@MIT (Massachusetts Institute of Technology). 5 indexed citations
15.
Kollar, Thomas, Stefanie Tellex, Deb Roy, & Nicholas Roy. (2010). Toward understanding natural language directions. 259–266. 67 indexed citations
16.
Kollar, Thomas, Stefanie Tellex, Deb Roy, & Nicholas Roy. (2010). Toward understanding natural language directions. 259–259. 75 indexed citations
17.
Kollar, Thomas & Nicholas Roy. (2009). Utilizing object-object and object-scene context when planning to find things. 2168–2173. 77 indexed citations
18.
Kollar, Thomas & Nicholas Roy. (2008). Efficient optimization of information-theoretic exploration in SLAM. National Conference on Artificial Intelligence. 1369–1375. 30 indexed citations
19.
Brown, Christopher C., et al.. (2004). Mabel: Extending Human Interaction and Robot Rescue Designs. UR Research (University of Rochester). 20–29. 2 indexed citations
20.
Kollar, Thomas, et al.. (2002). Mabel: building a robot designed for human interaction. UR Research (University of Rochester). 24–31. 4 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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