Max Robotics — Global Service Robot Directory + US Certification

About

Kui Fu is at the forefront of robotic grasping and manipulation, specializing in computer vision and deep learning for unstructured environments. His work tackles the critical challenge of enabling robots to perceive and grasp unknown objects efficiently and accurately. Fu’s major contributions include pioneering light-weight convolutional neural networks for generative grasping, as demonstrated in his most-cited work (29 citations), which introduces a quantized grasp quality generative neural network for pixel-level grasp planning. He has also advanced unseen object instance segmentation, proposing novel methods like the Taylor Neural Network and Fast UOIS with adaptive clustering to balance speed and precision in cluttered industrial settings. Beyond grasping, Fu contributes to surgical robotics, co-organizing the SAR-RARP50 challenge for tool segmentation and action recognition in robot-assisted prostatectomy. His research on express sorting robots further highlights his applied impact in logistics automation. With over 50 citations across his key papers, Fu’s work is shaping the next generation of intelligent robotic systems capable of operating reliably in the real world.