Yongpeng Jiang

Tsinghua University

Papers

6

Total Citations

47

H-Index

5

About

Yongpeng Jiang is a rising roboticist whose research lies at the intersection of dexterous manipulation, human-robot collaboration, and contact-rich control. His work addresses fundamental challenges in robotic interaction with deformable objects and constrained environments, with a focus on enabling robots to perform complex tasks that require both precision and adaptability. Jiang’s most cited paper, “Generalizable Whole-Body Global Manipulation of Deformable Linear Objects by Dual-Arm Robot in 3-D Constrained Environments” (2024, 17 citations), introduces a framework for manipulating cables and wires in cluttered spaces—a critical capability for manufacturing and service robotics. He has also made significant contributions to in-hand manipulation, notably as the champion of the RGMC competition with a solution for large-range precise object movement (2025, 6 citations). His work on contact-implicit model predictive control (2024, 8 citations) advances robust dexterous manipulation over long horizons, while his complementary framework for human-robot collaboration using mixed AR-haptic interfaces (2023, 8 citations) directly addresses the safety-efficiency tradeoff in cobots. Jiang’s research in robotic ultrasound scanning further demonstrates his commitment to developing safe, intuitive interaction control systems for real-world clinical applications. With over 47 total citations across his key publications, Jiang is establishing himself as a leading voice in the next generation of physically intelligent robotic systems.

Research Focus

Key Achievements

5
H-Index
6
Papers
47
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments
17 citations · 2024
📈 Most Prolific Year: 2024 (3 Papers)
🤝 Key Collaborators: 20
🏛 Institutions: Tsinghua University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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