Papers
9
Total Citations
289
H-Index
6
About
Yewei Huang is a robotics researcher whose work spans autonomous exploration, multi-robot systems, simultaneous localization and mapping (SLAM), and underwater autonomy. His most influential contribution, DiSCo-SLAM (2021, 112 citations), introduced a groundbreaking distributed multi-robot LiDAR SLAM framework that pioneered the use of lightweight Scan Context descriptors for efficient inter-robot data exchange, significantly advancing collaborative mapping capabilities. Complementing this, his work on deep reinforcement learning for autonomous exploration under uncertainty (2020, 74 citations) demonstrated how graph-based learning can enable robots to balance localization accuracy with information gain in unknown environments — a challenge central to real-world deployment. Huang has extended these contributions into particularly demanding domains, developing frameworks for cluttered underwater exploration (2022, 41 citations) and acoustic communication-efficient SLAM for sonar-equipped underwater robot teams (2022, 20 citations). His zero-shot reinforcement learning approach (2021, 18 citations) further showcases his interest in generalizable, transferable autonomy policies. More recently, he has tackled collision avoidance for autonomous surface vehicles using distributional reinforcement learning and Gaussian process motion planning for underwater vehicles navigating complex seafloor terrain. Across his body of work, Huang consistently addresses the practical intersection of uncertainty, communication constraints, and scalable autonomy in real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments41 citations · 2022
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- 9A new method of dead reckoning for differential drive mobile robots2 citations · 2010