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About

Alexis Linard is a researcher at the intersection of formal methods, robotics, and human-robot interaction. Their work centers on using Signal Temporal Logic (STL)—a rigorous specification language for spatio-temporal requirements—to enable safer and more interpretable robot behavior. Linard’s key contributions include developing methods for active learning of STL specifications from data, allowing robots to infer human motion patterns and preferences without explicit programming. Their 2023 paper on real-time RRT* with STL preferences (11 citations) bridges motion planning and formal logic, enabling robots to optimize trajectories under complex temporal constraints. Linard also pioneered the formalization of human trajectories in human-robot encounters, as seen in their work on multi-class STL inference (2022, 5 citations) and probabilistic STL models (2021, 5 citations). Notably, their 2021 paper "Should Robots Chicken?" (6 citations) explores the everyday collision-avoidance problem, using game-theoretic insights to improve robot navigation in crowded spaces. With a growing citation record and a focus on making robots both logically rigorous and socially aware, Linard is shaping how autonomous systems can safely and predictably interact with humans.