Dehua Pan

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About

Dehua Pan is a robotics researcher whose work focuses on advancing intelligent robotic systems for real-world applications, particularly in imitation learning, pipeline maintenance, and remote manipulation. Pan’s most cited paper, “A syntactic method for robot imitation learning of complex sequence task” (2023, 2 citations), introduces a novel approach that uses structural grammar to enhance robot generalization and reduce dependency on high-accuracy detectors, marking a significant contribution to imitation learning. In “Research on Structure Design and Intelligent Control of Pipeline Desilting Robot” (2024, 1 citation), Pan addresses the challenges of urban drainage maintenance by designing a robot capable of autonomous desilting, improving efficiency and safety in underground environments. Another notable work, “Study on Remote Operation Obstacle Avoidance of Robotic Arm by Fusing APF and RRT Algorithms” (2024, 1 citation), tackles latency and obstacle avoidance in teleoperation by fusing artificial potential fields with rapidly exploring random trees. Pan’s research demonstrates a strong commitment to bridging theoretical algorithms with practical robotics, offering solutions that enhance autonomy and reliability in complex, unstructured settings.