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About

Yudie Hu is a leading researcher in human-robot collaboration, with a focus on safe and adaptive automation for industrial manufacturing. Her work addresses critical challenges in deploying collaborative robots (cobots) in dynamic environments, where human safety and real-time adaptability are paramount. Hu’s most-cited paper, a comprehensive survey on safe human-robot collaboration for industrial settings (2023, 101 citations), has become a foundational resource for researchers and practitioners, synthesizing key safety standards and technologies. She further advanced the field with her adaptive obstacle avoidance approach for cobots in dynamic manufacturing (2021, 32 citations), enabling robots to navigate unpredictable workspaces without compromising efficiency. Hu also developed an improved task-parameterized learning from demonstrations method (2021, 21 citations), allowing cobots to learn complex tasks from human examples with greater precision. Most recently, she pioneered a novel framework for human-robot collaborative disassembly using brainwaves and an improved generative adversarial network (2024, 10 citations), opening new avenues for intuitive, brain-controlled robotics in recycling and remanufacturing. With over 160 total citations, Hu’s work is shaping the future of safe, intelligent, and human-centric industrial automation.