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About

Roberto Capobianco is an Italian robotics researcher whose work sits at the intersection of autonomous systems, human-robot interaction, and machine learning. His research has consistently focused on enabling robots to build richer, more meaningful representations of the environments in which they operate — a challenge that is fundamental to deploying autonomous robots in real-world, everyday settings. Among his most significant contributions is the development of semantic and interactive mapping frameworks, which allow robots to acquire environmental knowledge not only through their own sensors but also through natural interaction with human users. His 2016 work on "Living with Robots" (41 citations) and his earlier research on online semantic mapping (37 citations) established key methods for bridging perception and knowledge representation in dynamic environments. His 2018 paper on long-term mapping in dynamic environments (37 citations) further advanced SLAM systems capable of adapting to real-world complexity over extended periods. Capobianco has also explored machine learning for robot control, including spatio-temporal affordance maps for learning human-robot handovers, and contributed to European robotics benchmarking through the RoCKIn competition initiative. Collectively, his work — with over 190 cumulative citations — offers essential tools for building robots that are not just capable, but genuinely interactive and adaptable companions.