Stephen Tian

746 total citations · 1 hit paper
8 papers, 156 citations indexed

About

Stephen Tian is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Stephen Tian has authored 8 papers receiving a total of 156 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Control and Systems Engineering, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Stephen Tian's work include Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (3 papers) and Modular Robots and Swarm Intelligence (2 papers). Stephen Tian is often cited by papers focused on Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (3 papers) and Modular Robots and Swarm Intelligence (2 papers). Stephen Tian collaborates with scholars based in United States, Singapore and Germany. Stephen Tian's co-authors include Chelsea Finn, Mayur Mudigonda, Roberto Calandra, Dinesh Jayaraman, Frederik Ebert, Sergey Levine, Anirudha Majumdar, Weiyu Liu, Mac Schwager and Danny Driess and has published in prestigious journals such as The International Journal of Robotics Research, Science Robotics and International Conference on Learning Representations.

In The Last Decade

Stephen Tian

5 papers receiving 150 citations

Hit Papers

align trajectories

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.

Foundation models in robotics: Applications, challenges, and the future

2024 73 citations Roya Firoozi, Stephen Tian et al. The International Journal of Robotics Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Stephen Tian United States	4	78	47	47	44	36	8	156
Frederik Ebert United States	4	67 0.9×	46 1.0×	45 1.0×	37 0.8×	23 0.6×	5	115
Maria Bauzá United States	7	128 1.6×	82 1.7×	82 1.7×	29 0.7×	37 1.0×	8	193
Francois R. Hogan Canada	6	113 1.4×	25 0.5×	55 1.2×	20 0.5×	51 1.4×	19	175
Sanket Kamthe United Kingdom	3	122 1.6×	22 0.5×	52 1.1×	62 1.4×	41 1.1×	4	177
Arthur Allshire Switzerland	4	80 1.0×	16 0.3×	32 0.7×	35 0.8×	23 0.6×	5	108
Cewu Lu China	5	41 0.5×	12 0.3×	19 0.4×	31 0.7×	36 1.0×	12	122
Gerard Canal United Kingdom	8	71 0.9×	17 0.4×	18 0.4×	79 1.8×	58 1.6×	28	199
Simon Stepputtis United States	8	112 1.4×	11 0.2×	30 0.6×	78 1.8×	60 1.7×	20	213
Tu-Hoa Pham United States	4	82 1.1×	11 0.2×	35 0.7×	42 1.0×	41 1.1×	11	145
Ruohan Gao United States	6	29 0.4×	58 1.2×	16 0.3×	51 1.2×	107 3.0×	21	230

Countries citing papers authored by Stephen Tian

Since Specialization

Citations

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

Fields of papers citing papers by Stephen Tian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Tian

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Tian. A scholar is included among the top collaborators of Stephen Tian 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 Stephen Tian. Stephen Tian 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:

8 of 8 papers shown

1.

Tian, Stephen, Tobias Pfaff, Cheston Tan, et al.. (2025). A review of learning-based dynamics models for robotic manipulation. Science Robotics. 10(106). eadt1497–eadt1497.

2.

Dong, Zhao, Zhaoyang Lv, Yunzhi Zhang, et al.. (2025). Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset. 753–763.

3.

Firoozi, Roya, Stephen Tian, Anirudha Majumdar, et al.. (2024). Foundation models in robotics: Applications, challenges, and the future. The International Journal of Robotics Research. 44(5). 701–739. 73 indexed citations breakdown →

4.

Guo, Michelle, et al.. (2024). Learning to Design 3D Printable Adaptations on Everyday Objects for Robot Manipulation. 824–830.

5.

Ai, Bo, Stephen Tian, Haochen Shi, et al.. (2024). RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing. 4 indexed citations

6.

Tian, Stephen, et al.. (2023). Multi-Object Manipulation via Object-Centric Neural Scattering Functions. 9021–9031. 4 indexed citations

7.

Tian, Stephen, Suraj Nair, Frederik Ebert, et al.. (2021). Model-Based Visual Planning with Self-Supervised Functional Distances. International Conference on Learning Representations. 1 indexed citations

8.

Tian, Stephen, Frederik Ebert, Dinesh Jayaraman, et al.. (2019). Manipulation by Feel: Touch-Based Control with Deep Predictive Models. 818–824. 74 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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