Hang Yin

805 total citations · 1 hit paper
49 papers, 506 citations indexed

About

Hang Yin is a scholar working on Control and Systems Engineering, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Hang Yin has authored 49 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Control and Systems Engineering, 15 papers in Aerospace Engineering and 14 papers in Artificial Intelligence. Recurrent topics in Hang Yin's work include Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (9 papers) and Adaptive Control of Nonlinear Systems (5 papers). Hang Yin is often cited by papers focused on Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (9 papers) and Adaptive Control of Nonlinear Systems (5 papers). Hang Yin collaborates with scholars based in China, Sweden and Switzerland. Hang Yin's co-authors include Danica Kragić, Aude Billard, Miao Li, Kenji Tahara, Xianlin Huang, Hongqian Lu, Mårten Björkman, Michael C. Welle, Martina Lippi and Alessandro Marino and has published in prestigious journals such as IEEE Transactions on Vehicular Technology, Neural Networks and IEEE Transactions on Robotics.

In The Last Decade

Hang Yin

40 papers receiving 491 citations

Hit Papers

align trajectories sort by overperformance

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.

Modeling, learning, perception, and control methods for deformable object manipulation

2021 181 citations Hang Yin, Danica Kragić et al. Science Robotics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hang Yin China	11	307	150	143	81	76	49	506
Ulrich Hillenbrand Germany	13	346 1.1×	216 1.4×	269 1.9×	79 1.0×	219 2.9×	27	706
Xavi Gratal Sweden	7	414 1.3×	202 1.3×	179 1.3×	52 0.6×	79 1.0×	8	549
Nadia Figueroa United States	16	312 1.0×	205 1.4×	193 1.3×	74 0.9×	63 0.8×	49	634
Zainan Jiang China	11	213 0.7×	108 0.7×	120 0.8×	20 0.2×	133 1.8×	48	402
Sebastian Höfer Germany	10	223 0.7×	88 0.6×	216 1.5×	90 1.1×	107 1.4×	21	447
Renaud Detry Belgium	18	616 2.0×	319 2.1×	248 1.7×	261 3.2×	161 2.1×	55	874
Bruno Vilhena Adorno Brazil	14	399 1.3×	244 1.6×	173 1.2×	24 0.3×	97 1.3×	47	592
Bowen Wen United States	9	284 0.9×	97 0.6×	254 1.8×	74 0.9×	129 1.7×	24	518
Kuan‐Ting Yu Taiwan	7	316 1.0×	180 1.2×	216 1.5×	52 0.6×	172 2.3×	11	497

Countries citing papers authored by Hang Yin

Since Specialization

Citations

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

Fields of papers citing papers by Hang Yin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hang Yin

This figure shows the co-authorship network connecting the top 25 collaborators of Hang Yin. A scholar is included among the top collaborators of Hang Yin 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 Hang Yin. Hang Yin 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

Yin, Hang, et al.. (2025). Generative Video Semantic Communication via Multimodal Semantic Fusion With Large Model. IEEE Transactions on Vehicular Technology. 75(1). 1701–1706. 1 indexed citations

Zhou, Peng, et al.. (2024). Interactive Perception for Deformable Object Manipulation. IEEE Robotics and Automation Letters. 9(9). 7763–7770. 2 indexed citations

Yin, Wenjie, Yi Yu, Hang Yin, Danica Kragić, & Mårten Björkman. (2024). Scalable Motion Style Transfer with Constrained Diffusion Generation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(9). 10234–10242. 1 indexed citations

Yin, Wenjie, Hang Yin, Kim Baraka, Danica Kragić, & Mårten Björkman. (2023). Multimodal dance style transfer. Machine Vision and Applications. 34(4). 6 indexed citations

Yin, Hang, et al.. (2022). Embedding Koopman Optimal Control in Robot Policy Learning. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 13392–13399. 1 indexed citations

Lippi, Martina, et al.. (2022). Enabling Visual Action Planning for Object Manipulation Through Latent Space Roadmap. IEEE Transactions on Robotics. 39(1). 57–75. 10 indexed citations

Yin, Hang, et al.. (2021). Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation. IEEE Robotics and Automation Letters. 6(4). 8583–8590. 11 indexed citations

Antonova, Rika, et al.. (2021). Dynamic Environments with Deformable Objects. Neural Information Processing Systems. 4 indexed citations

Yin, Hang, et al.. (2021). Leveraging hierarchy in multimodal generative models for effective cross-modality inference. Neural Networks. 146. 238–255. 6 indexed citations

10.

Yin, Hang, et al.. (2021). Modeling, learning, perception, and control methods for deformable object manipulation. Science Robotics. 6(54). 181 indexed citations breakdown →

11.

Yin, Hang, et al.. (2021). Learning Stable Normalizing-Flow Control for Robotic Manipulation. KTH Publication Database DiVA (KTH Royal Institute of Technology). 7 indexed citations

12.

Yin, Hang, et al.. (2021). Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 5741–5748. 11 indexed citations

13.

Lippi, Martina, Michael C. Welle, Hang Yin, et al.. (2020). Benchmarking Bimanual Cloth Manipulation. IEEE Robotics and Automation Letters. 5(2). 1111–1118. 55 indexed citations

14.

Yin, Hang, Xu Zhang, Xianlin Huang, & Hongqian Lu. (2018). Two novel robust tracking control strategies for eliminating aircraft wing rock. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering. 233(8). 3042–3055. 7 indexed citations

15.

Gao, Yong, et al.. (2013). Control Strategy for Space Manipulator Based on Genetic Algorithm Self-Tuning. Advanced materials research. 706-708. 695–699.

16.

Yin, Hang. (2011). Friction compensation using observers with time-varying gain. Dianji yu kongzhi xuebao.

17.

Yin, Hang. (2010). Modeling of High Accuracy Local Geomagnetic Field Base on Rectangular Harmonic Analysis. Journal of Astronautics. 3 indexed citations

18.

Yin, Hang. (2009). Lunar surface hazard detection by 2D maximum entropy thresholding and ellipse fitting. Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University. 1 indexed citations

19.

Yin, Hang. (2007). T-S fuzzy modeling based on particle swarm optimization algorithms. Ha'erbin gongye daxue xuebao.

20.

Yin, Hang. (2007). Polarized-light-based navigation in three-dimensional space. Optical Technique. 1 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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