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About

Yuan-Pao Hsu is a robotics researcher whose work centers on intelligent navigation, human-robot interaction, and reinforcement learning for autonomous mobile systems. His major contributions include pioneering a novel associative architecture for the Cerebellar Model Articular Controller (CMAC) that replaces traditional hash-coding with content addressable memory, significantly reducing computational demands for motion control. Hsu also developed hybrid navigation techniques for four-wheeled omni-directional tour-guide robots, enabling robust point stabilization and trajectory tracking. In the realm of assistive robotics, he designed an active mobile robotic assistant integrated with RFID-based intelligent spaces to support elderly individuals in dynamic indoor environments. His work on learning acceleration is notable: he combined CMAC with Q-learning and prioritized sweeping to create a Dyna agent that dramatically shortens training time, and later introduced the Dyna-QPC algorithm for real-world control tasks. With over 36 citations across his most influential papers, Hsu’s research has advanced practical robot autonomy, bridging theoretical learning methods with deployable systems for service and guidance applications.