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About

Claude Sammut is a prominent Australian robotics and artificial intelligence researcher whose work spans autonomous navigation, human-robot interaction, and machine learning for robotic systems. Based at the University of New South Wales (UNSW), Sammut has made substantial contributions to the field of rescue robotics, most notably through his highly cited work on human-robot interfaces for Urban Search and Rescue (USAR) applications, which has garnered 92 citations and remains a foundational reference for practitioners designing practical mobile robot systems. His research into behavioural cloning — teaching robots to navigate complex and unstructured environments by learning from demonstration — reflects a consistent commitment to bridging machine learning and real-world robotic deployment, including challenging rough terrain scenarios. Sammut has also advanced quadruped locomotion, relational learning for robot tool-use, and terrain feature extraction from range imagery. His team's involvement in the RoboCup Sony Legged League further demonstrates his breadth across competitive and applied robotics. More recently, his work on graph-based transformer networks for trajectory prediction signals an engagement with cutting-edge deep learning methods relevant to autonomous driving and crowd-aware robotics. Across more than two decades, Sammut's research has consistently addressed the gap between theoretical AI and practical robotic autonomy.