Yuan-Chih Chen
Papers
1
Total Citations
6
H-Index
1
About
Yuan-Chih Chen is a researcher at the intersection of robotics, cognitive science, and deep learning, with a focus on endowing humanoid robots with human-like decision-making. His most cited work, "Deep Belief Network–Based Learning Algorithm for Humanoid Robot in a Pitching Game" (2019, 6 citations), introduces a novel cognition learning algorithm that integrates a deep belief network with inertia weight Particle Swarm Optimization. Drawing inspiration from Daniel Kahneman’s *Thinking, Fast and Slow*, Chen models the robot’s brain using two systems—System 1 for fast, intuitive responses and System 2 for slower, analytical reasoning—to improve performance in dynamic tasks like pitching. This interdisciplinary approach bridges artificial intelligence and psychology, offering a framework for more adaptive robotic behavior. While his citation count reflects an emerging career, Chen’s work stands out for its creative synthesis of cognitive theory and machine learning, paving the way for robots that can learn and react more naturally in real-world environments. His contributions are particularly relevant for researchers exploring embodied cognition and bio-inspired robotics.
Research Focus
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Top Papers
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