Alex Sleat

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About

Alex Sleat is a rising researcher at the intersection of robotics, artificial intelligence, and human-robot interaction. Their work primarily focuses on two key areas: enabling robots to understand and execute complex spatio-temporal tasks using formal logic, and investigating the cognitive mechanisms that underpin human-robot social dynamics. Sleat’s most impactful contribution, “Real-Time RRT* with Signal Temporal Logic Preferences” (2023, 11 citations), introduces a novel framework that integrates Signal Temporal Logic (STL) into real-time motion planning. This allows robots to not only satisfy hard safety constraints but also optimize for soft user-defined preferences, a critical step toward more intuitive and reliable autonomous systems. In parallel, their work “Can the robot ‘see’ what I see? Robot gaze drives attention depending on mental state attribution” (2023, 5 citations) explores how humans attribute mental states to robots, demonstrating that inferred intentions significantly influence gaze-following behavior. This research bridges cognitive science and robotics, offering insights for designing more socially aware machines. Sleat’s interdisciplinary approach is shaping how robots plan, perceive, and interact in human-centered environments.