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About

Saeid Amiri is a robotics and artificial intelligence researcher whose work sits at the intersection of automated planning, machine learning, and human-robot interaction. His research focuses on enabling robots to reason, learn, and make decisions in complex, uncertain, and open-world environments — a challenge that demands integrating symbolic knowledge with modern data-driven methods. Amiri's most influential contribution, "Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds" (2023, 53 citations), demonstrates his pioneering effort to combine large language models with classical planning frameworks, allowing robots to operate beyond the rigid assumptions of closed-world systems. His LCORPP framework (2020, 25 citations) further established him as a key voice in sequential decision-making under uncertainty, blending supervised learning with probabilistic reasoning. His work on multi-modal predicate identification (2018, 23 citations) advanced how robots perceive and learn object properties through sensory exploration, while his research on goal-oriented human-robot dialog (2019, 16 citations) highlighted pathways for robots to continuously improve their language understanding through interaction. Across his career, Amiri has consistently pushed toward robots that are adaptive, communicative, and capable of grounding abstract knowledge in real-world perception — accumulating over 140 citations and shaping a growing body of work in embodied AI and task planning.