Naila Zareen

Papers

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About

Naila Zareen’s research lies at the fascinating intersection of cognitive robotics, language acquisition, and artificial intelligence, with a particular focus on how machines can understand and ground abstract concepts. Her most-cited work, "Theoretical accounts to practical models: Grounding phenomenon for abstract words in cognitive robots" (2016), tackles a fundamental challenge in AI: enabling robots to move beyond concrete, sensorimotor associations and grasp the meaning of words like “freedom” or “justice.” By bridging theoretical cognitive science with practical computational models, Zareen proposes frameworks that allow robots to learn abstract language through social interaction, context, and metaphorical reasoning—a critical step toward more human-like communication. Though her citation count is currently modest, her work is pioneering in a niche that is rapidly gaining importance as AI systems are deployed in complex social environments. Zareen’s contributions are particularly notable for their interdisciplinary rigor, drawing from developmental psychology, linguistics, and robotics to create models that are both theoretically sound and implementable. For students and researchers exploring the frontiers of embodied cognition and human-robot interaction, her research offers a compelling roadmap for teaching machines not just to name, but to understand.