Papers
45
Total Citations
500
H-Index
12
About
Hongmin Wu is a prominent robotics researcher whose work spans motion planning, robot learning, manipulation control, and intelligent manufacturing. Best known for developing recurrent neural network (RNN)-based approaches to robotic control, Wu's 2021 paper on simultaneous obstacle avoidance and target tracking for manipulators has accumulated 85 citations, establishing him as a leading voice in real-time motion planning methodologies. His research extends to cooperative kinematic control of multiple redundant manipulators and adaptive optimal impedance control, reflecting a deep commitment to solving complex, multi-agent robotic challenges. Wu has also made significant contributions to robot learning and introspection. His framework for skill learning from complex, long-horizon tasks and his multimodal anomaly detection systems demonstrate a sophisticated understanding of how robots can autonomously interpret and adapt to dynamic environments. His work on Bayesian nonparametric hidden Markov models further highlights his interdisciplinary approach, bridging probabilistic modeling and robotics. In industrial applications, Wu has advanced robotic surface machining through uncertainty-aware error modeling and hierarchical compensation methods, directly improving manufacturing precision. With over 300 cumulative citations across his most impactful papers, Wu's research consistently bridges theoretical innovation and practical robotic deployment, making him an influential figure for students and researchers in intelligent robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 3A Framework of Robot Skill Learning From Complex and Long-Horizon Tasks30 citations · 2021
- 4Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking30 citations · 2020
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