Papers
14
Total Citations
529
H-Index
9
About
Zhimin Hou is a leading researcher in robotic assembly and manipulation, with a primary focus on developing intelligent, learning-based control strategies for complex industrial tasks. Their most significant contributions lie in advancing reinforcement learning (RL) and hierarchical reinforcement learning (HRL) for robotic peg-in-hole assembly—a notoriously difficult challenge requiring precise contact management. Hou’s work on Feedback Deep Deterministic Policy Gradient with Fuzzy Reward (179 citations) pioneered a model-driven deep RL approach that enables robots to autonomously complete multiple peg-in-hole assemblies without complex contact modeling. Their subsequent research on data-efficient HRL (84 citations) and fuzzy logic-driven variable time-scale prediction (68 citations) further improved sample efficiency and generalization across different assembly scenarios. Hou also authored a comprehensive survey comparing contact model-based and model-free strategies (84 citations), providing a foundational reference for the field. Beyond assembly, Hou has contributed to exoskeleton design and cable-driven gravity compensation mechanisms, demonstrating versatility in robotic systems. With over 500 total citations and multiple high-impact publications in IEEE Transactions, Hou’s work bridges the gap between theoretical RL advances and practical robotic applications, making them a key figure in intelligent manufacturing and automation research.
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