Jhe-Syun Li

Papers

Total Citations

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About

Jhe-Syun Li is a robotics researcher whose work centers on bipedal locomotion and reinforcement learning, with a particular focus on enabling robots to achieve dynamic balance and adaptive walking. His most cited paper, “Gait balance of biped robot based on reinforcement learning” (2013, 5 citations), introduces a novel approach where a biped robot learns to walk without any prior knowledge of its dynamics model, using the Q-learning algorithm. The key contribution lies in the robot’s ability to maintain balance on one leg—a critical challenge in humanoid robotics—through trial-and-error learning rather than pre-programmed control. This work demonstrates how reinforcement learning can replace traditional model-based control for complex, unstable tasks. Li’s research has implications for the development of more autonomous and resilient humanoid robots capable of navigating uneven terrain. While his citation count is modest, his early adoption of learning-based methods for gait control represents a foundational step in a field that has since grown rapidly. His work is particularly valuable for students and researchers interested in the intersection of machine learning and physical robotics, offering a clear example of how simple algorithms can solve complex real-world problems.