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About

Thane Somers is a leading researcher at the intersection of marine robotics and human-robot interaction, with a focus on autonomous systems for scientific data collection. His work centers on developing coactive learning algorithms that enable robots to efficiently gather information by learning from human experts’ preferences and feedback. Somers’ most influential contribution is his 2015 paper on human-robot planning and learning for marine data collection, which has garnered 38 citations and laid the groundwork for semi-autonomous underwater vehicle (sAUV) deployments. He demonstrated the practical impact of his research through the ocean deployment and testing of a semi-autonomous AUV, integrating commercial ROV and AUV systems to enhance navigation performance in real-world offshore environments. More recently, Somers has advanced the field by proposing mixed-type query selection methods that combine preference and rating queries to better capture scientists’ goals for robotic decision-making. His work directly addresses the challenge of enabling robots to autonomously collect high-value scientific data while remaining responsive to human expertise, making him a key figure in the development of intelligent, collaborative marine robotics systems.