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About

Yifei Shao is a versatile robotics researcher whose work spans autonomous systems, safety-critical motion planning, and multi-sensor perception. Drawing on expertise across aerial robotics, simultaneous localization and mapping (SLAM), and human-robot interaction, Shao has built a body of research that bridges theoretical rigor with real-world applicability. His most-cited work, "UAVs for Forestry" (2024, 24 citations), demonstrates his ability to deploy autonomous aerial robots for metric-semantic mapping and tree diameter estimation—advancing precision forestry through intelligent perception. His 2019 contribution to multisensor SLAM (12 citations) reflects a commitment to accessibility in robotics education, making cutting-edge localization techniques approachable for undergraduate researchers despite hardware barriers. Shao has made notable strides in safe motion planning through his REFINE framework (8 citations), which leverages reachability analysis and zonotopes to provide real-time safety guarantees for autonomous vehicles—a critical challenge in the field. His work on reinforcement learning safety layers and constraint-aware human intent estimation further underscores a unifying theme: enabling robots to operate safely and collaboratively in dynamic, unpredictable environments. With over 55 cumulative citations across diverse subfields, Shao represents an emerging force in trustworthy autonomous robotics.