Papers
16
Total Citations
133
H-Index
7
About
Haopeng Hu is a robotics researcher whose work centers on robot learning from demonstration (LfD), assembly automation, and human-robot skill transfer, with a particular focus on the demanding challenges of 3C (Computer, Communication, and Consumer Electronics) manufacturing. His most-cited contribution, "Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing" (2021, 36 citations), provided a critical systematic assessment of motion capture technologies as tools for translating human expertise into robotic behavior — filling a notable gap in the LfD literature. Building on this foundation, Hu has developed innovative frameworks that enable robots to learn precise, contact-rich assembly skills directly from human demonstration, including work on Sequential Assembly Movement Primitives and Programming by Demonstration with real-time human correction. His research also extends into computer vision, with contributions to high-precision 6D pose estimation and monocular-vision-based robotic charging systems for electric vehicles. Through reinforcement learning approaches such as actor-critic search strategies for peg-in-hole tasks, Hu has consistently pushed the boundaries of flexible automation. Collectively accumulating over 115 citations, his body of work offers practical, impactful pathways for deploying intelligent, adaptable robots in real-world industrial assembly environments.
Research Focus
Key Achievements
Top Papers
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- 7Fast 6D object pose estimation of shell parts for robotic assembly7 citations · 2021
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- 10Trajectory Planning of Collaborative Robot for 3C Products Assembly5 citations · 2019