Lian Fu

State Key Laboratory of Industrial Control Technology, The University of Tokyo

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About

Lian Fu is a robotics researcher whose work centers on advancing robotic manipulation, particularly in industrial and unstructured environments. Their primary research areas include bin-picking, object grasping, and category-level articulation estimation, with a focus on developing robust, efficient solutions for real-world applications. Fu’s major contribution is the design of a "Fast and Robust Bin-picking System for Densely Piled Industrial Objects" (2020, 8 citations), which addresses the critical challenge of enabling robots to reliably grasp objects from cluttered bins—a task essential for automation in manufacturing and logistics. This work stands out for its balance of speed and robustness, overcoming the limitations of prior approaches that were either fragile or computationally expensive. More recently, Fu introduced CAPT (2024, 2 citations), a novel transformer-based architecture that estimates joint parameters of articulated objects from a single point cloud. This end-to-end method represents a significant step forward in enabling robots to interact with doors, drawers, and other hinged objects without prior knowledge of their category. Fu’s research is notable for its practical impact, directly addressing bottlenecks in industrial automation and pushing the boundaries of perception-driven manipulation.