Chris Paxton

1.7k total citations
38 papers, 835 citations indexed

About

Chris Paxton is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Chris Paxton has authored 38 papers receiving a total of 835 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Control and Systems Engineering, 20 papers in Computer Vision and Pattern Recognition and 17 papers in Artificial Intelligence. Recurrent topics in Chris Paxton's work include Robot Manipulation and Learning (21 papers), Robotic Path Planning Algorithms (11 papers) and Reinforcement Learning in Robotics (10 papers). Chris Paxton is often cited by papers focused on Robot Manipulation and Learning (21 papers), Robotic Path Planning Algorithms (11 papers) and Reinforcement Learning in Robotics (10 papers). Chris Paxton collaborates with scholars based in United States, China and Japan. Chris Paxton's co-authors include Gregory D. Hager, Dieter Fox, Maya Çakmak, Yan Ding, Shiqi Zhang, Xiaohan Zhang, Marin Kobilarov, Yu-Wei Chao, Colin Lea and Tucker Hermans and has published in prestigious journals such as IEEE Robotics and Automation Letters, Advanced Robotics and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Chris Paxton

34 papers receiving 801 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chris Paxton United States	17	422	363	345	91	81	38	835
Scott Niekum United States	15	527 1.2×	240 0.7×	469 1.4×	59 0.6×	112 1.4×	45	900
Joni Pajarinen Finland	13	374 0.9×	240 0.7×	391 1.1×	96 1.1×	77 1.0×	52	875
Lorenz Mösenlechner Germany	13	413 1.0×	277 0.8×	394 1.1×	101 1.1×	143 1.8×	22	815
Luís Seabra Lopes Portugal	16	256 0.6×	340 0.9×	389 1.1×	139 1.5×	71 0.9×	92	800
Hangxin Liu China	16	283 0.7×	220 0.6×	199 0.6×	90 1.0×	91 1.1×	59	725
Dirk Kraft Denmark	15	470 1.1×	283 0.8×	272 0.8×	190 2.1×	60 0.7×	65	794
Alberto Finzi Italy	16	273 0.6×	248 0.7×	347 1.0×	160 1.8×	107 1.3×	68	840
Abdeslam Boularias United States	16	392 0.9×	253 0.7×	377 1.1×	84 0.9×	60 0.7×	59	782
Sarah Osentoski United States	15	543 1.3×	226 0.6×	471 1.4×	70 0.8×	166 2.0×	26	906
Harish Ravichandar United States	12	446 1.1×	165 0.5×	253 0.7×	28 0.3×	108 1.3×	34	721

Countries citing papers authored by Chris Paxton

Since Specialization

Citations

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

Fields of papers citing papers by Chris Paxton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris Paxton

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Liu, Peiqi, Zhaohui Guo, Soumith Chintala, et al.. (2025). Dynamem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation. 13346–13355.

Gervet, Théophile, Dhruv Shah, Chris Paxton, et al.. (2024). GOAT: GO to Any Thing. 11 indexed citations

Liu, Peiqi, et al.. (2024). Demonstrating OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics. 21 indexed citations

Kawaharazuka, Kento, et al.. (2024). Real-World Robot Applications of Foundation Models: A Review. arXiv (Cornell University). 1 indexed citations

Liu, Weiyu, Yilun Du, Tucker Hermans, Sonia Chernova, & Chris Paxton. (2023). StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects. 17 indexed citations

Shafiullah, Nur Muhammad Mahi, Chris Paxton, Lerrel Pinto, Soumith Chintala, & Arthur Szlam. (2023). CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory. 49 indexed citations

Wu, Hongtao, et al.. (2022). Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10756–10763. 8 indexed citations

Paxton, Chris, et al.. (2022). Learning Perceptual Concepts by Bootstrapping From Human Queries. IEEE Robotics and Automation Letters. 7(4). 11260–11267. 5 indexed citations

Fan, Linxi, et al.. (2022). Pre-Trained Language Models for Interactive Decision-Making. 31199–31212.

10.

Yang, Wei, Balakumar Sundaralingam, Chris Paxton, et al.. (2022). Model Predictive Control for Fluid Human-to-Robot Handovers. 2022 International Conference on Robotics and Automation (ICRA). 13 indexed citations

11.

Chao, Yu-Wei, Chris Paxton, Xiang Yu, et al.. (2022). HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers. 2022 International Conference on Robotics and Automation (ICRA). 6941–6947. 13 indexed citations

12.

Paxton, Chris, et al.. (2021). Automated Generation of Robotic Planning Domains from Observations. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 6732–6738. 22 indexed citations

13.

Paxton, Chris, et al.. (2021). Reactive Human-to-Robot Handovers of Arbitrary Objects. 3118–3124. 52 indexed citations

14.

Paxton, Chris, et al.. (2021). Reactive Long Horizon Task Execution via Visual Skill and Precondition Models. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 5717–5724. 9 indexed citations

15.

Paxton, Chris, et al.. (2019). Conditional Driving from Natural Language Instructions. 540–551. 5 indexed citations

16.

Katyal, Kapil D., et al.. (2019). Uncertainty-Aware Occupancy Map Prediction Using Generative Networks for Robot Navigation. 5453–5459. 28 indexed citations

17.

Paxton, Chris, et al.. (2017). CoSTAR: Instructing collaborative robots with behavior trees and vision. 564–571. 104 indexed citations

18.

Bohren, Jonathan, et al.. (2016). Semi-autonomous telerobotic assembly over high-latency networks. 149–156. 14 indexed citations

19.

Paxton, Chris, et al.. (2016). Do what i want, not what i did: Imitation of skills by planning sequences of actions. 3778–3785. 12 indexed citations

20.

Lea, Colin, et al.. (2015). A framework for end-user instruction of a robot assistant for manufacturing. 6167–6174. 68 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.

Explore authors with similar magnitude of impact