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About

Rina Tse is a robotics and artificial intelligence researcher whose work spans terrain estimation, autonomous navigation, and human-robot collaboration. Her early contributions focused on improving robotic perception, most notably through her 2012 paper on Markov Random Field-based terrain estimation, which garnered 21 citations and advanced the field by formally incorporating sensor pose and measurement uncertainties into 2.5D map fusion. This work remains her most influential contribution to autonomous systems. A significant thread throughout Tse's career is the challenge of meaningful human-robot communication. Her research on probabilistic belief sharing — including frameworks using Dirichlet Process Mixtures and structured natural language generation — explores how humans and robots can effectively exchange uncertain, complex information during cooperative tasks. These works collectively address a critical barrier in real-world deployment of collaborative robotic systems, particularly in scenarios like search and rescue where mixed teams must coordinate under significant constraints. Tse's earlier work on KFLANN-based place field navigation and Bayesian intention recognition demonstrates her long-standing interest in biologically inspired and probabilistic approaches to robot intelligence. With research spanning nearly two decades and a total citation record reflecting steady engagement from the robotics community, Tse has made meaningful contributions to making robots more perceptive, communicative, and trustworthy partners for human collaborators.