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About

Chibum Lee is a robotics researcher whose work bridges intelligent control, autonomous navigation, and additive manufacturing. His primary research areas include reinforcement learning for robotics, model predictive control, and the development of specialized robotic systems. Lee’s most significant contribution is his 2021 paper on reinforcement learning-based dynamic obstacle avoidance and path planning integration, which has garnered 94 citations—demonstrating its strong impact on the field of autonomous mobile robotics. This work addresses a critical challenge in real-world robot navigation by enabling adaptive, real-time obstacle avoidance without pre-mapped environments. Lee has also made notable contributions to robotic manipulation and control, including path tracking for differential drive robots using nonlinear model predictive control, and the development of a dual-arm service robot integrating stereo vision and 6-axis manipulators—a senior design project that showcases practical, low-cost collaborative robotics. Additionally, his early work on a three-dimensional chocolate printer (2017) highlights his versatility, exploring the material science and extrusion conditions necessary for 3D printing with chocolate. Lee’s research portfolio demonstrates a commitment to advancing both theoretical control methods and applied robotic systems, making him a well-rounded contributor to modern robotics.