Qingtong Wu

National University of Defense Technology

Papers

Total Citations

H-Index

About

Qingtong Wu is a leading researcher in multi-robot systems and cooperative artificial intelligence, with a focus on enabling adaptive, intelligent collaboration among heterogeneous robotic teams. Their major contributions lie at the intersection of multi-agent reinforcement learning and meta-learning, where they pioneered the Collaborative Relationship Meta Reinforcement Learning (CRMRL) framework. This work, cited 9 times, addresses the critical challenge of dynamic robot type changes in multi-robotic systems—allowing robots to rapidly adapt to unforeseen variations in team composition without retraining from scratch. Wu also advanced computation offloading for mobile robotics, demonstrating how cooperative offloading strategies can overcome the severe resource constraints of individual robots, a foundational problem for deploying complex applications on edge devices. Their research is notable for bridging theoretical advances in meta-learning with practical deployment challenges in robotics, earning recognition for its impact on scalable, real-world multi-robot coordination. With a growing citation record, Wu’s work is shaping the future of autonomous systems that must operate reliably in unpredictable environments—a key frontier for search-and-rescue, warehouse automation, and collaborative drone fleets.