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About

Dr. PU Jin-yun is a rising force in intelligent robotics and autonomous systems, whose work is defining the next generation of resilient, adaptive machines. Her research centers on the intersection of deep reinforcement learning and neural network control, applied to three critical domains: autonomous underwater vehicles (AUVs), tracked mobile robots, and lower extremity exoskeletons. Her most cited work, a 2022 study on AUV control, tackles the life-or-death challenge of active self-rescue, using deep reinforcement learning to enable underwater vehicles to survive complex missions—a contribution with 12 citations that speaks to its foundational importance. Dr. Pu has also pioneered the use of finite-time convergence zeroing neural networks for robust trajectory tracking in tracked mobile robots, achieving 6 citations for her 2023 paper, and has advanced force tracking control in human-robot interaction for exoskeletons, a 2024 study already garnering 3 citations. Her innovative integration of extended state observers with neural network control further demonstrates her ability to solve real-world problems of uncertainty and disturbance. With a growing citation footprint and a clear trajectory toward enhancing robotic survivability and human augmentation, Dr. Pu is a researcher to watch in the field of intelligent control systems.