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About

Chavdar Papazov is a leading researcher in robotics, whose work bridges the gap between perception and manipulation in complex, real-world environments. His primary research areas include 3D object recognition, mobile manipulation, and human-robot interaction, with a strong focus on enabling robots to operate reliably outside of structured labs. Papazov’s most influential contribution is his 2012 work on rigid 3D geometry matching for grasping, which has garnered 162 citations. This paper introduced a robust geometric descriptor for object recognition and pose estimation in cluttered, occluded scenes, relying solely on 3D geometry rather than appearance—a foundational approach for industrial and service robotics. He also pioneered vision-based augmented telemanipulation for remote assembly over high-latency networks, a framework that intelligently augments human motion commands. More recently, Papazov has advanced mobile manipulation with a metrics-driven system demonstrated in an unmodified grocery store, and achieved sub-second motion planning to Cartesian targets using large-scale dynamic roadmaps. His work is notable for its practical, systems-level focus, pushing robots from the lab into the wild.