Papers
8
Total Citations
75
H-Index
5
About
Shuling Dai is a leading researcher at the intersection of robotics, optimization, and human-robot interaction. Her work focuses on enabling robots to operate intelligently and safely in dynamic, real-world environments, with key contributions in trajectory planning, reinforcement learning, and haptic feedback systems. Dai’s most impactful work, “Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction” (28 citations), pioneered efficient motion generation for collaborative robots, ensuring safe and fluid human-robot coexistence. She further advanced the field with a non-convex global optimization approach for serial manipulators (26 citations), addressing complex, high-dimensional motion challenges. Her research extends to learning-based methods, including continuous trajectory planning via learning optimization and guiding reinforcement learning with vision-language models, bridging the gap between simulation and reality. Notably, Dai has also explored singularity-free workspace analysis for parallel robots and developed real-time trajectory generation for haptic feedback manipulators in virtual cockpit systems. Her recent work on image segmentation-driven sim-to-real reinforcement learning for peg-in-hole assembly demonstrates her commitment to practical, industry-relevant automation. With a growing citation record and a focus on integrating machine learning with classical robotics, Shuling Dai is shaping the future of adaptive, intelligent robotic systems.
Research Focus
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