Hsin-Jung Hsu

Papers

Total Citations

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About

Hsin-Jung Hsu is a researcher in robotics and human-robot interaction, with a primary focus on humanoid robot locomotion and imitation learning. Their most cited work, "Humanoid robot gait imitation" (2014, 5 citations), introduces a novel learning model that enables humanoid robots to achieve stable walking by imitating human gaits. The research leverages a Kinect sensor to capture human skeleton data during walking, then extracts key postures from a full walking cycle. By applying Q-Learning, a reinforcement learning technique, the robot learns to replicate these postures, resulting in more natural and adaptive gait patterns. This work bridges the gap between human motion analysis and robotic control, offering a practical framework for teaching robots complex motor skills through demonstration. Although the citation count is modest, the study represents a foundational step in applying machine learning to humanoid robotics, particularly in the context of low-cost sensor integration and real-time imitation. Hsu's contributions are valuable for students and researchers interested in the intersection of computer vision, reinforcement learning, and biomechanics for autonomous robotic systems.