Chenhui Pan
Papers
7
Total Citations
61
H-Index
4
About
Chenhui Pan is an emerging robotics researcher whose work focuses on autonomous navigation, off-road mobility, and machine learning for wheeled robotic systems operating in extreme environments. His most significant contribution lies in advancing the field of "vertically challenging terrain" navigation — pushing wheeled robots beyond conventional flat-surface limitations to traverse rugged boulders, steep slopes, and complex off-road landscapes that were previously considered impassable. Pan's research portfolio demonstrates remarkable breadth across the full autonomy stack. He has developed novel platforms, datasets, and algorithms for extreme wheeled mobility, pioneered terrain-attentive kinodynamic modeling across all six degrees of freedom, and applied reinforcement learning to achieve smooth, collision-free trajectories on unpredictable terrain. His self-supervised representation learning framework, VertiCoder, further shows his interest in generalizable, data-efficient approaches to robot learning. His most cited work (24 citations) establishing foundational datasets and algorithms for vertically challenging terrain has become a key reference in off-road robotics. With additional contributions to passive perception under extreme low-light conditions, Pan is building a cohesive research identity at the intersection of robot learning, perception, and mobility — positioning himself as a notable voice in next-generation autonomous systems research.
Research Focus
Key Achievements
Top Papers
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