Corentin Rasle

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About

Corentin Rasle investigates the critical intersection of human-robot interaction and trust calibration, with a focus on preventing overtrust in autonomous systems during high-stakes scenarios. His most-cited work, "Reducing Overtrust in Failing Robotic Systems" (2019, 12 citations), demonstrates through a blindfolded maze experiment that vocal error warnings can effectively moderate users' misplaced confidence in malfunctioning robots. This research addresses a fundamental challenge in robotics: the tendency for humans to over-rely on automated systems, even when those systems are clearly failing. By empirically testing simple yet impactful interventions like auditory alerts, Rasle’s work provides actionable insights for designing safer human-robot collaborations. His contributions are particularly relevant for emergency response, autonomous vehicles, and assistive technologies, where overtrust can lead to critical failures. Though early in his career, Rasle’s focus on trust dynamics positions him at the forefront of efforts to build more transparent and reliable robotic systems—a vital pursuit as AI-driven machines become increasingly integrated into daily life.