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About

Shi Bai is a robotics researcher whose work sits at the intersection of autonomous exploration, mobile robot navigation, and machine learning. His research has made significant contributions to the challenge of enabling robots to intelligently map and navigate unknown environments without human intervention. Bai's most influential work, "Information-theoretic exploration with Bayesian optimization" (2016, 132 citations), established a principled framework for guiding mobile robots toward maximally informative locations using Bayesian optimization — a landmark contribution that shaped subsequent research in autonomous exploration. Building on this foundation, he advanced the field by incorporating deep learning techniques, demonstrating in "Toward autonomous mapping and exploration for mobile robots through deep supervised learning" (2017, 61 citations) and "Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning" (2019, 53 citations) that robots could learn exploration strategies directly from experience. His later work pushed toward zero-shot generalization, allowing policies trained in simulation to transfer to novel real-world environments, reflecting a growing emphasis on scalable, practical robotics. More recently, Bai has expanded into robot manipulation, exploring self-supervised grasping through augmented reality teleoperation. Across his career, his research has accumulated over 300 citations, reflecting sustained influence on how autonomous robots perceive, learn, and act in complex, uncertain environments.