Max Robotics — Global Service Robot Directory + US Certification

About

Meiqing Wu’s research sits at the intersection of computer vision, lifelong learning, and edge computing—a triad aimed at giving robots the ability to continuously learn from their environment without catastrophic forgetting. Her early work on corner detection introduced an enhanced low-complexity pruning method that remains cited for its efficiency in real-time vision systems. Wu’s most significant contributions, however, center on lifelong robotic vision. She was instrumental in organizing the IROS 2019 Lifelong Robotic Vision Challenge, which drew over 150 teams and established the OpenLORIS-object benchmark—a dataset now foundational for evaluating continual object recognition. Her report on the competition’s top eight methods has guided subsequent research in the field. Wu also broke new ground in hardware-software co-design with her proposal of an FPGA-based edge accelerator for lifelong deep learning using streaming Linear Discriminant Analysis, enabling real-time adaptation on resource-constrained platforms like drones. With over 30 citations across her most-cited works, Wu’s contributions are shaping how autonomous systems learn persistently in the wild, bridging algorithmic innovation with practical deployment.