Minlue Wang

Papers

Total Citations

H-Index

About

Minlue Wang’s research lies at the intersection of robotics, artificial intelligence, and decision-making under uncertainty, with a particular focus on information gathering tasks. Wang’s major contributions center on improving how robots plan and execute missions in noisy, resource-constrained environments—such as search and rescue operations, Mars rover navigation, and environmental monitoring. By integrating execution monitoring into robot plans, Wang has advanced the ability of autonomous systems to adapt in real time, ensuring that critical information is collected even when sensors are imperfect or time is limited. Wang’s work also explores the role of explicit ontological knowledge-bases, demonstrating how structured world models can enhance a robot’s reasoning and task performance. Though early in their career, Wang’s most cited paper (4 citations) on improving robot plans through execution monitoring has laid groundwork for more robust autonomous systems. Collectively, Wang’s research addresses fundamental challenges in partially observable Markov decision processes (POMDPs), offering practical solutions for robots that must act intelligently under constraints. This work is particularly relevant for students and researchers interested in bridging theoretical planning models with real-world robotic applications.