Bo Ling

Southeast University, Hangzhou Dianzi University

Papers

Total Citations

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About

Bo Ling’s research sits at the intersection of social robotics, autonomous navigation, and automated control systems, with a focus on bridging simulation and real-world performance. In their most cited work, “SocialGAIL: Faithful Crowd Simulation for Social Robot Navigation” (2024, 5 citations), Ling addresses a critical bottleneck in robot learning: the need for realistic, socially-aware crowd simulations. By leveraging generative adversarial imitation learning, they developed a framework that produces faithful pedestrian behaviors, enabling robots to train more effectively for safe and natural navigation in dense human environments. This contribution is particularly impactful for the growing field of human-robot interaction, where trust and safety are paramount. Ling’s second notable paper, “Automatic PLC Control Logic Generation Method Based on SysML System Design Model” (2025, 2 citations), tackles industrial automation by proposing a method to automatically generate Programmable Logic Controller code from system design models—a step toward reducing development costs and accelerating deployment in manufacturing. Though early in their career, Ling’s work demonstrates a clear trajectory toward making autonomous systems both more intelligent and more practical, with implications for service robots, smart factories, and beyond.