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About

Aaron Mininger is a researcher specializing in interactive task learning, grounded language acquisition, and human-robot interaction. His work centers on enabling robotic agents to learn from natural human instruction — acquiring not just procedural task knowledge, but also perceptual, semantic, and linguistic understanding through situated, mixed-initiative dialogue. Mininger's most influential contribution, "Acquiring Grounded Representations of Words with Situated Interactive Instruction," has garnered 39 citations and exemplifies his core research thrust: building robots that can meaningfully interpret and learn from the words humans use in context. Much of his work is grounded in the Soar cognitive architecture, with his robot Rosie — capable of object manipulation and indoor navigation — serving as a testbed for integrating language comprehension, task execution, and real-world grounding through systems like the Embodied Construction Grammar-based comprehender Lucia. With a body of work spanning over a decade, from foundational 2012 studies on grounded language learning to 2021 expansions of explanation-based task learning, Mininger has made consistent contributions to the emerging field of Interactive Task Learning. His research directly addresses the practical challenge of creating AI agents that collaborate naturally and adaptively with human partners.