Max Robotics — Global Service Robot Directory + US Certification

About

Xuning Yang is a robotics researcher whose work centers on enabling safe, efficient, and intuitive human-robot interaction, particularly for mobile robot navigation. Her major contributions lie at the intersection of collision avoidance, teleoperation, and intent-driven control. She pioneered a probabilistic approach to reactive collision avoidance using real-time Gaussian Mixture Model maps (23 citations), which allows robots to navigate unknown, cluttered environments with greater memory and computational efficiency than discrete map representations. In teleoperation, Yang developed a framework for online adaptation that models user intent to dynamically adjust available actions, significantly improving operator performance (22 citations). Her later work introduced hierarchical frameworks and biased incremental action sampling to generate intent-aligned trajectories, ensuring robots complete tasks with ease while avoiding obstacles. With over 80 total citations across her most-cited papers, Yang’s research has advanced the practicality of assisted teleoperation and precision local navigation—her 2025 work on achieving sub-meter accuracy for docking and inspection tasks pushes the boundaries of what autonomous navigation can accomplish. Her contributions are particularly notable for making complex robotic systems more accessible and effective for real-world applications.