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About

Xavier Perrin’s research lies at the intersection of assistive robotics, human-robot interaction (HRI), and brain-computer interfaces, with a focus on enabling semi-autonomous navigation for users with severe motor impairments. His major contribution is the development of a “brain-coupled” interaction framework that allows individuals to guide assistive devices—such as intelligent wheelchairs—using low-throughput interfaces, including EEG-based brain signals. In his most cited work (2010, 112 citations), Perrin demonstrated how a robot could propose navigational actions while a human user, via brain signals, either accepts or rejects them, effectively blending machine autonomy with human intent. His comparative study (2008, 20 citations) systematically evaluated visual, auditory, and tactile feedback modalities, revealing how different sensory channels affect user performance and cognitive load during HRI. Perrin also pioneered Bayesian controllers and learning algorithms that predict user destinations and habits, enabling robots to adapt to individual preferences over time. His work has been instrumental in making assistive robots more responsive and intuitive, directly impacting the design of shared-control systems for people with disabilities.