Xinyue Zhu

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About

Xinyue Zhu is a researcher at the forefront of human-robot interaction and autonomous robotic systems, with a focus on making robots more intuitive, adaptive, and safe. Her work spans three key areas: empathetic decision-making for assistive robots, visual perception for robot protection, and language-driven task planning. Zhu’s most impactful contribution is the Human-Robot Empathy Decision-Making Model (HREDM), which enables service robots to move beyond passive command execution and proactively interpret user emotional states—a critical advancement for assistive robotics, particularly for disabled individuals. This work has already garnered 8 citations since its 2024 publication. She also pioneered a SLIC-based camouflage region selection method for patrol robots, allowing them to autonomously hide in dangerous environments by analyzing color, texture, and spatial features. Most recently, Zhu introduced GRID, a scene-graph-based framework that leverages Large Language Models to ground natural language instructions in environmental context, overcoming the limitations of raw image-based approaches. Her research demonstrates a consistent commitment to bridging the gap between robotic perception, cognition, and real-world deployment, with each project addressing a distinct challenge in autonomous decision-making.