Papers

5

Total Citations

133

H-Index

3

About

Wenzhe Tong is a robotics researcher whose work pushes the boundaries of human-robot interaction and proprioceptive state estimation. His most impactful contribution is the development of a **robotic guide dog** that uses leash-guided hybrid physical interaction to lead visually-impaired individuals through narrow, cluttered spaces—a breakthrough that has garnered **102 citations**. This work addresses a critical gap in assistive robotics by moving beyond bulky wheeled platforms with rigid canes to a more natural, intuitive guidance system. Tong’s expertise extends to **state estimation for mobile robots**, where he has pioneered fully proprioceptive slip-velocity-aware methods using Invariant Kalman Filtering and Disturbance Observers (17 citations). He has also made notable advances in **tensegrity robotics**, designing a variable stiffness quasi-direct drive cable-actuated robot and developing proprioceptive state estimation with geometric constraints (8 and 3 citations, respectively). These contributions enable robots to navigate uncertain environments with greater autonomy and resilience. With a career spanning from early work on fuzzy logic for multi-robot coordination (1999) to cutting-edge tensegrity systems (2025), Tong’s research consistently addresses fundamental challenges in robot perception, control, and assistive technology.

Research Focus

Key Achievements

3
H-Index
5
Papers
133
Total Citations
27
Avg Citations/Paper
🏆 Most Cited Paper
Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interaction
102 citations · 2021
📈 Most Prolific Year: 2025 (2 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: University of California, Berkeley, University of Michigan–Ann Arbor, Polytechnique Montréal

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago