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Xu-Yang Dai is a researcher at the forefront of autonomous robotics, specializing in active perception, safe navigation, and simultaneous localization and mapping (SLAM). Their work addresses critical challenges in enabling robots to operate intelligently in unknown and complex environments. A key contribution is the development of learning-based frameworks for active exploration, such as a generative adversarial imitation learning approach for camera view planning, which significantly improves a robot’s ability to gather information autonomously. In the domain of safe navigation, Dai introduced Safe-Nav, a method that uses RGB-D sensing to prevent navigation failures in uncertain settings, directly tackling the integration of obstacle avoidance and path planning. Their research also extends to olfactory robotics, where they proposed an infotaxis-based strategy for odor source searching using advanced TDLAS gas sensors, enhancing efficiency over traditional methods. With a total of over 43 citations across their most-cited works, Dai’s contributions to uncertainty-driven view planning and feature-based monocular SLAM have provided robust solutions for low-texture and illumination-changing environments, marking them as a rising innovator in intelligent robotic systems.