Hailey Baez

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About

Hailey Baez is a researcher in robotics and artificial intelligence, with a primary focus on knowledge representation and symbolic task planning for autonomous manipulation. Her most cited work introduces and evaluates the Functional Object-Oriented Network (FOON), a graph-based knowledge representation that enables robots to generate sequential task plans by retrieving structured task trees. This contribution addresses a fundamental challenge in robotics: bridging high-level symbolic reasoning with low-level physical actions. By formalizing how robots can access and utilize object-function relationships, Baez’s research lays critical groundwork for more adaptable and intelligent robotic systems. Her work has garnered attention within the AI and robotics communities, with her top-cited paper accumulating 2 citations—a meaningful start for an emerging scholar. Baez’s contributions are particularly notable for their potential to enhance robot learning from human demonstrations and improve generalization across diverse manipulation tasks. For students and researchers interested in cognitive robotics, planning, and human-robot interaction, Baez’s research offers a compelling framework for understanding how machines can reason about everyday objects and actions.