Ya Wu

Papers

Total Citations

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About

Ya Wu is a leading researcher in robotics and human-robot interaction, with a primary focus on pedestrian trajectory prediction and continual learning for autonomous systems. Their most cited work, "Continual Pedestrian Trajectory Learning With Social Generative Replay" (2022, 20 citations), tackles a critical challenge in mobile robotics: enabling robots to adapt to diverse environments where pedestrian motion patterns shift over time. Wu’s key contribution lies in developing a social generative replay framework that allows models to retain knowledge of past environments while learning new ones—a breakthrough that enhances the safety and efficiency of robots operating in dynamic, real-world settings. This work addresses the fundamental problem of catastrophic forgetting in neural networks, ensuring that robots can seamlessly transition between different operational contexts without losing prior learning. Wu’s research has significant implications for autonomous navigation in crowded spaces, such as shopping malls, airports, and urban streets. By pioneering methods that combine continual learning with social dynamics, Ya Wu is shaping the future of adaptive, context-aware robotics, making autonomous systems more reliable and responsive to human behavior.