Je-Ting Li
Papers
1
Total Citations
20
H-Index
1
About
Je-Ting Li’s research lies at the intersection of robotics, intelligent control, and human-machine interaction, with a particular focus on developing adaptive systems that learn from experience. Their most cited work, “Design of the neural-fuzzy compensator for a billiard robot” (2004, 20 citations), exemplifies this approach by creating a system that mimics human learning to improve robotic billiards performance. Li developed a predictable hitting error model based on recorded data, then integrated neural networks and fuzzy logic to compensate for inaccuracies, enabling the robot to refine its skills over time. This contribution is notable for bridging theoretical control methods with practical, real-world robotic tasks, demonstrating how machines can achieve precision through adaptive learning. While citation counts are modest, the work’s interdisciplinary nature—combining robotics, artificial intelligence, and sports engineering—highlights Li’s role in advancing autonomous systems that require both computational intelligence and physical dexterity. Their research offers valuable insights for students and researchers interested in neural-fuzzy systems, robotic manipulation, and the design of learning-based compensators for complex dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Design of the neural-fuzzy compensator for a billiard robot20 citations · 2004