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About

Moslem Kazemi is a robotics researcher whose work sits at the intersection of autonomous manipulation, visual servoing, and robot perception. His research has fundamentally advanced how robots perceive, plan, and interact with unstructured environments — particularly in the challenging domain of grasping and manipulating unknown objects. Kazemi's most influential contribution, an integrated autonomous robotic manipulation system (2012, 120 citations), demonstrated tight coupling between perception, planning, and control to enable robots to grasp and manipulate objects with minimal human supervision. Building on this foundation, he pioneered interactive perception techniques that allow robots to segment, track, and model unknown articulated objects and clear cluttered piles autonomously — work that collectively attracted over 230 citations. His research on object affordances further enabled robots to learn and exploit physical properties of objects for intelligent action selection. In visual servoing, Kazemi made significant strides by incorporating randomized kinodynamic planning and global path planning to produce robust, collision-aware camera trajectories in complex environments, with his review of visual servoing path planning (2010, 60 citations) becoming a key reference in the field. His later work on markerless arm tracking demonstrated practical closed-loop control using commodity depth sensors. Across his career, Kazemi has shaped modern expectations for intelligent, adaptable robotic systems operating in real-world conditions.