Max Robotics — Global Service Robot Directory + US Certification

About

Yi Lee’s research lies at the intersection of robotic manipulation, computer vision, and energy-efficient AI, with a focus on bridging low-power hardware and practical automation. His most cited work, “A Simple Robotic Eye-In-Hand Camera Positioning and Alignment Control Method Based on Parallelogram Features” (2018, 13 citations), introduces an elegant, computationally light approach to camera-guided pick-and-place tasks. By encoding parallelogram features into 3D objects, Lee’s method enables precise pose estimation and alignment without heavy processing, making it ideal for resource-constrained robotic systems. This contribution has been recognized as a practical solution for industrial automation, particularly in scenarios requiring real-time, low-latency control. Lee also contributed to “The 2020 Low-Power Computer Vision Challenge” (2021, 3 citations), a landmark competition that spurred innovation in energy-efficient AI for mobile and edge devices, including drones and robots. His work underscores a commitment to making computer vision accessible and sustainable, addressing the growing demand for battery-friendly computation. With a career focused on simplifying complex robotic tasks and advancing green AI, Lee’s research continues to influence both academic robotics and real-world deployment, offering students and engineers a blueprint for efficient, scalable automation.