Papers
1
Total Citations
14
H-Index
1
About
Ruihan Pan is a researcher at the forefront of cognitive robotics and human-robot interaction, with a focus on bridging the gap between artificial intelligence and human-like learning. Their most-cited work, "Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction" (2020, 14 citations), introduces a novel framework that mimics neural processes to enable robots to acquire procedural knowledge through active, interactive learning. This contribution is pivotal in advancing how machines understand and execute complex tasks by leveraging human guidance, moving beyond passive data ingestion. Pan’s research integrates principles from neuroscience, machine learning, and robotics, offering a pathway to more adaptive and intuitive AI systems. While their citation impact is still growing, the work has already attracted attention for its innovative approach to embodied cognition and lifelong learning. Pan’s achievements highlight a commitment to creating AI that learns as humans do—through curiosity, interaction, and incremental understanding—making their research a cornerstone for future developments in human-robot collaboration and autonomous systems.
Research Focus
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Top Papers
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