Katie Browne

University of Nevada, Reno, Ames Research Center

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About

Katie Browne is a leading researcher in human-robot interaction and autonomous navigation, with a focus on enabling robots to operate effectively in complex, real-world environments. Her work bridges the gap between machine perception and human intent, most notably through her pioneering 2012 paper on deep networks for predicting human intent with respect to objects (19 citations). In this foundational study, she introduced a system using stacked denoising autoencoders to infer and predict human intentions during human-robot collaboration, establishing a key framework for intent recognition that has influenced subsequent work in assistive and collaborative robotics. More recently, Browne has made significant contributions to space robotics. In 2024, she co-authored the first annotated benchmark datasets for free-flyer visual-inertial localization and mapping, collected aboard the International Space Station using NASA’s Astrobee robots (9 citations). This work provides critical resources for advancing autonomous navigation in zero-gravity environments, supporting future intra-vehicular robotics for space exploration. With a career spanning foundational machine learning for interaction to applied space robotics, Browne’s research demonstrates a unique ability to tackle both theoretical and deployment challenges, making her a notable figure in the evolution of intelligent, autonomous systems.