Zhenyuan Yuan
Papers
13
Total Citations
77
H-Index
5
About
Zhenyuan Yuan is a robotics and autonomous systems researcher whose work spans machine learning, multi-robot coordination, and human-robot interaction. His research sits at the intersection of deep learning, probabilistic modeling, and control theory, with a particular focus on enabling robots to learn, collaborate, and operate safely in complex real-world environments. Yuan's most influential contribution is his end-to-end imitation learning framework combining convolutional and LSTM neural networks for autonomous ground robots (2019, 21 citations), demonstrating how spatiotemporal deep architectures can enable robots to learn directly from human demonstrations. He has also advanced distributed machine learning for robot networks, proposing communication-aware Gaussian process regression algorithms that allow multi-robot systems to collaboratively learn in real time (2020, 10 citations). A distinctive thread in Yuan's work is robot-assisted crowd evacuation, where he develops mathematical models and control strategies enabling small robot teams to guide large human crowds to safety — a challenging problem with clear humanitarian implications. His more recent contributions extend into federated reinforcement learning and secure perception-driven control, addressing both generalization and cybersecurity challenges in autonomous systems. Collectively, his publications reflect a researcher steadily building toward robust, trustworthy autonomy for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 8Multirobot-Guided Crowd Evacuation: Two-Scale Modeling and Control4 citations · 2024
- 9dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems3 citations · 2022
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