Max Robotics — Global Service Robot Directory + US Certification

About

Xiaoting Dong is a leading researcher at the intersection of robotics and artificial intelligence, with a primary focus on advancing industrial automation through deep reinforcement learning and continual learning. Her work addresses critical challenges in agile manufacturing, particularly in enabling robots to adapt to unstructured environments and solve complex, sequential tasks without catastrophic forgetting. Dong’s most cited paper, “Design and implementation of agent-based robotic system for agile manufacturing: A case study of ARIAC 2021” (16 citations), showcases her ability to translate theoretical frameworks into practical, competition-tested systems. She has also pioneered novel approaches to sim-to-real transfer learning, as demonstrated in her Kalman Filter-based method (9 citations), which reduces the sample inefficiency of deep RL in physical systems. Her recent work on mitigating catastrophic forgetting (7 citations) introduces a guided policy search enhanced with memory-aware synapses, a significant step toward lifelong learning in robots. Additionally, Dong’s optimization of robotic task sequencing and trajectory planning (7 citations) highlights her talent for integrating traditionally separate problems to achieve synergistic performance gains. With a growing citation record and a clear trajectory toward solving foundational issues in robot autonomy, Dong is a rising voice in the field of intelligent manufacturing.