Chih-Kai Cheng

Papers

Total Citations

H-Index

About

Chih-Kai Cheng is a robotics researcher whose work centers on the intersection of reinforcement learning and robotic motion planning. His primary research area focuses on developing intelligent control strategies for robotic arms, particularly in optimizing velocity profiles to enhance precision and efficiency in automated tasks. His most-cited paper, "The Robotic Arm Velocity Planning Based on Reinforcement Learning" (2023), has garnered 8 citations, demonstrating early recognition for his innovative approach to integrating machine learning with traditional kinematic constraints. This work addresses a critical challenge in industrial robotics: enabling manipulators to adapt their motion in real-time without relying on pre-programmed trajectories. By leveraging reinforcement learning, Cheng's methodology allows robotic arms to learn optimal velocity curves through trial and error, improving both safety and performance in dynamic environments. Though early in his career, his contributions signal a promising direction for adaptive robotics, with potential applications in manufacturing, logistics, and human-robot collaboration. His research is particularly valuable for students and engineers seeking to bridge the gap between classical control theory and modern AI-driven automation.