Sehoon Ha

4.7k total citations · 1 hit paper
62 papers, 1.3k citations indexed

About

Sehoon Ha is a scholar working on Biomedical Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sehoon Ha has authored 62 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Biomedical Engineering, 31 papers in Control and Systems Engineering and 26 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sehoon Ha's work include Robotic Locomotion and Control (29 papers), Reinforcement Learning in Robotics (18 papers) and Robot Manipulation and Learning (17 papers). Sehoon Ha is often cited by papers focused on Robotic Locomotion and Control (29 papers), Reinforcement Learning in Robotics (18 papers) and Robot Manipulation and Learning (17 papers). Sehoon Ha collaborates with scholars based in United States, South Korea and Switzerland. Sehoon Ha's co-authors include Katsu Yamane, C. Karen Liu, Joohyung Kim, Jie Tan, Alexander Alspach, Stelian Coros, Yuting Ye, Yunfei Bai, Naoki Yokoyama and Sumit Jain and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and ACM Transactions on Graphics.

In The Last Decade

Sehoon Ha

57 papers receiving 1.3k citations

Hit Papers

Iterative Training of Dynamic Skills Inspired by Human Co... 2014 2026 2018 2022 2014 100 200 300 400
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. Iterative Training of Dynamic Skills Inspired by Human Coaching Techniques
    2014 402 citations Sehoon Ha et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sehoon Ha United States 17 510 494 433 253 178 62 1.3k
Abhinav Gupta United States 12 694 1.4× 687 1.4× 326 0.8× 323 1.3× 93 0.5× 27 1.4k
Peter Allen United States 22 517 1.0× 886 1.8× 675 1.6× 149 0.6× 211 1.2× 57 1.7k
Ning Xi United States 20 392 0.8× 541 1.1× 465 1.1× 206 0.8× 443 2.5× 130 2.0k
Arjun Singh United States 8 698 1.4× 957 1.9× 504 1.2× 232 0.9× 162 0.9× 14 1.6k
Jun Takamatsu Japan 21 615 1.2× 629 1.3× 365 0.8× 193 0.8× 202 1.1× 186 1.6k
Tsutomu Hasegawa Japan 20 641 1.3× 713 1.4× 507 1.2× 105 0.4× 275 1.5× 195 1.5k
David Navarro-Alarcón Hong Kong 18 469 0.9× 676 1.4× 556 1.3× 71 0.3× 228 1.3× 103 1.4k
Chyi‐Yeu Lin Taiwan 22 306 0.6× 480 1.0× 255 0.6× 137 0.5× 213 1.2× 125 1.4k
Roberto Martín-Martín United States 21 1.0k 2.1× 992 2.0× 260 0.6× 296 1.2× 141 0.8× 48 1.8k
Berk Çallı United States 17 558 1.1× 1.2k 2.4× 737 1.7× 228 0.9× 243 1.4× 41 1.7k

Countries citing papers authored by Sehoon Ha

Since Specialization
Citations

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

Fields of papers citing papers by Sehoon Ha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sehoon Ha

This figure shows the co-authorship network connecting the top 25 collaborators of Sehoon Ha. A scholar is included among the top collaborators of Sehoon Ha 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 Sehoon Ha. Sehoon Ha 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
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Liu, Fobang, Yujun Cai, Sehoon Ha, et al.. (2025). Opt2Skill: Imitating Dynamically-Feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation. IEEE Robotics and Automation Letters. 10(11). 12261–12268.
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Kim, Seong-Yong, Seung‐Woo Kim, Sehoon Ha, et al.. (2024). Understanding human-robot proxemic norms in construction: How do humans navigate around robots?. Automation in Construction. 164. 105455–105455. 3 indexed citations
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Yokoyama, Naoki, et al.. (2024). VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation. 42–48. 38 indexed citations
7.
Ha, Sehoon, et al.. (2024). Emotion-Behavior Interplay in Human Animal-Robot Interaction (HARI). 727–731. 1 indexed citations
8.
Lee, Ung Hee, Patrick W. Franks, Jie Tan, et al.. (2023). User preference optimization for control of ankle exoskeletons using sample efficient active learning. Science Robotics. 8(83). eadg3705–eadg3705. 15 indexed citations
9.
Hwangbo, Jemin, et al.. (2023). Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion. IEEE Robotics and Automation Letters. 8(11). 7799–7806. 8 indexed citations
10.
Yu, Wenhao, et al.. (2023). How to Train Your Guide Dog: Wayfinding and Safe Navigation with Human-Robot Modeling. 221–225. 4 indexed citations
11.
Essa, Irfan, et al.. (2023). Cascaded Compositional Residual Learning for Complex Interactive Behaviors. IEEE Robotics and Automation Letters. 8(8). 4601–4608. 8 indexed citations
12.
Yokoyama, Naoki, Alexander Clegg, Eric Undersander, et al.. (2023). ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation. IEEE Robotics and Automation Letters. 9(1). 779–786. 17 indexed citations
13.
Won, Jungdam, et al.. (2023). ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters. Seoul National University Open Repository (Seoul National University). 1–11. 9 indexed citations
14.
Soto, Daniel, et al.. (2022). Real‐Time Remodeling of Granular Terrain for Robot Locomotion. SHILAP Revista de lepidopterología. 4(12). 7 indexed citations
15.
Tan, Jie, et al.. (2022). Learning to Navigate Sidewalks in Outdoor Environments. IEEE Robotics and Automation Letters. 7(2). 3906–3913. 25 indexed citations
16.
Ha, Sehoon. (2020). Quadrupedal robots trot into the wild. Science Robotics. 5(47). 6 indexed citations
17.
Ha, Sehoon, Peng Xu, Zhenyu Tan, Sergey Levine, & Jie Tan. (2020). Learning to Walk in the Real World with Minimal Human Effort. 1110–1120. 16 indexed citations
18.
Yu, Wenhao, Jie Tan, Yunfei Bai, Erwin Coumans, & Sehoon Ha. (2020). Learning Fast Adaptation With Meta Strategy Optimization. IEEE Robotics and Automation Letters. 5(2). 2950–2957. 56 indexed citations
19.
Ha, Sehoon, Stelian Coros, Alexander Alspach, Joohyung Kim, & Katsu Yamane. (2018). Computational co-optimization of design parameters and motion trajectories for robotic systems. The International Journal of Robotics Research. 37(13-14). 1521–1536. 60 indexed citations
20.
Ha, Sehoon, Stelian Coros, Alexander Alspach, et al.. (2018). Computational Design of Robotic Devices From High-Level Motion Specifications. IEEE Transactions on Robotics. 34(5). 1240–1251. 46 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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