Tsung-Chuan Huang

Papers

Total Citations

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About

Tsung-Chuan Huang is a researcher in robotics and human-robot interaction, with a primary focus on humanoid robot locomotion and gait imitation. His most cited work, "Humanoid robot gait imitation" (2014), introduces a novel learning model that enables humanoid robots to walk stably by imitating human gaits. The method leverages a Kinect sensor to capture human skeleton data during walking, then extracts key postures from a full gait cycle. By applying Q-learning, a reinforcement learning technique, the robot refines its walking pattern to achieve greater stability and natural motion. This work bridges computer vision and machine learning to address a fundamental challenge in humanoid robotics: translating human movement into robot control. With 5 citations, this paper has contributed to the growing field of imitation learning for humanoid robots. Huang’s research is particularly valuable for students and researchers interested in bio-inspired robotics, as it demonstrates a practical pipeline from human motion capture to autonomous robot behavior. His work underscores the potential of combining sensory input with adaptive learning algorithms to create more agile and human-like robotic systems.