Max Robotics — Global Service Robot Directory + US Certification

About

Shengfan Wang is a leading researcher in robotic manipulation and computer vision, with a focus on enabling robots to perform complex, real-world grasping and assembly tasks. His work addresses critical challenges in logistics, industrial automation, and service robotics. Wang’s most notable contribution is an efficient fully convolutional neural network for generating pixel-wise robotic grasps from high-resolution RGB-D images (2019, 58 citations), a method that significantly improves grasp precision in cluttered environments. He has also pioneered techniques for handling mixed rigid and soft objects, such as grasping items tangled with towels (2020, 13 citations), and developed a system for assembling randomly placed parts using only a single robot arm and parallel-jaw gripper (2020, 11 citations). His vision-based picking system for automatic express package dispatching (2019, 5 citations) demonstrates practical deployment of deep learning in logistics. Wang’s work bridges the gap between perception and action, advancing robotic autonomy in unstructured settings. His research is widely cited for its practical impact on warehouse automation and industrial assembly, making him a key figure in the field of intelligent robotics.