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About

Qingwei Dong is a robotics researcher whose work focuses on bridging the gap between simulation and real-world deployment for industrial automation. His key research areas include reinforcement learning, sim-to-real transfer, and continual learning for robotic systems. Dong’s most impactful contribution is the design and implementation of an agent-based robotic system for agile manufacturing, demonstrated through his work on the ARIAC 2021 competition, which has earned 16 citations. He further advanced the field with a Kalman Filter-based one-shot sim-to-real transfer learning approach (9 citations), enabling deep reinforcement learning algorithms to be applied to physical robots without extensive real-world data collection—a critical breakthrough given equipment lifespan and safety constraints. Most recently, Dong has addressed the challenge of catastrophic forgetting in robot continual learning, proposing a guided policy search method enhanced with memory-aware synapses (7 citations). This work enables industrial robots to sequentially solve multiple interrelated tasks while retaining previously learned skills. His research is particularly relevant for developing more adaptable, lifelong-learning robots capable of handling complex, unstructured manufacturing environments.