Max Robotics — Global Service Robot Directory + US Certification

About

Chungkeun Lee’s research lies at the intersection of computer vision and robotic control, with a focus on enabling autonomous systems to perceive and interact with their environments. His most cited work, “Comparison of faster R-CNN models for object detection” (48 citations), established a benchmark for evaluating real-time object detection architectures—a critical capability for autonomous robots operating under computational constraints. Lee advanced robot skill acquisition through “Learning and Generalization of Dynamic Movement Primitives by Hierarchical Deep Reinforcement Learning from Demonstration” (16 citations), where he combined imitation learning with deep RL to enable robots to generalize complex movements from a single demonstration. In “Position-based monocular visual servoing of an unknown target using online self-supervised learning” (15 citations), he tackled the challenge of estimating target position from a single camera without prior knowledge, enabling adaptive visual servo control. His work on “Vision-based Target Tracking for a Skid-steer Vehicle using Guided Policy Search with Field-of-view Constraint” (8 citations) introduced a policy learning method that respects physical constraints like camera field-of-view, producing more practical mobile robot control. Collectively, Lee’s contributions bridge perception, learning, and control—pushing toward robots that can see, learn, and act in unstructured environments.