About

Weiwei Wan is a prominent robotics researcher whose work spans robotic manipulation, perception, and autonomous systems. His research has made substantial contributions to several interconnected fields, including vision-based tactile sensing, robotic grasping, assembly planning, and human-robot collaboration. Wan's most cited work, a 2022 review of vision-based tactile sensor hardware (150 citations), has become an essential reference for researchers developing next-generation robotic perception systems. His 2017 papers collectively established him as a leading voice in robotic caging, affordance theory, and assembly sequence planning, each garnering 50–65 citations and shaping how robots understand and interact with objects in structured tasks. His 2020 work on human-in-the-loop manipulation planning (69 citations) reflects a forward-thinking approach to collaborative robotics, emphasizing the complementary roles of human judgment and machine intelligence. Notably, Wan has also tackled practical industrial challenges, including peg-in-hole assembly under uncertainty using deep learning and multi-view imaging. His 2017 contribution on autonomous indoor navigation (102 citations) further demonstrates his breadth across perception and mobility. Altogether, Wan's research offers both theoretical depth and real-world applicability, making his work indispensable reading for students and practitioners advancing intelligent robotic systems.

Research Focus

Key Achievements

25
H-Index
160
Papers
2,333
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
Hardware Technology of Vision-Based Tactile Sensor: A Review
150 citations · 2022
📈 Most Prolific Year: 2019 (25 Papers)
🤝 Key Collaborators: 236
🏛 Institutions: The University of Osaka, National Institute of Advanced Industrial Science and Technology, Osaka University of Economics, Carnegie Mellon University, Osaka Health Science University, Peking University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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