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About

Anahita Mohseni-Kabir is a robotics and artificial intelligence researcher whose work spans human-robot interaction, task learning, multi-agent systems, and adaptive software. She is best known for her pioneering contributions to hierarchical task learning, particularly her development of algorithms enabling robots to acquire complex task models from a single human demonstration — a landmark achievement reflected in her most-cited work, "Interactive Hierarchical Task Learning from a Single Demonstration" (2015, 86 citations). This research introduced mixed-initiative interaction frameworks with bidirectional communication, dramatically reducing the burden on human teachers while improving robot adaptability. Mohseni-Kabir has consistently pushed the boundaries of robot intelligence, exploring how semantic knowledge networks can inform task adaptation, how model-based approaches can yield long-lasting adaptive software systems, and how multi-agent reinforcement learning can enable decentralized mobile robot navigation in dynamic environments. Her work on simultaneous learning of task hierarchies and primitives further consolidated her reputation as a thoughtful contributor to robot autonomy. With nearly 200 cumulative citations across a decade of research, her portfolio demonstrates sustained impact across multiple subfields. Students interested in robot learning, human-robot collaboration, or adaptive autonomous systems will find her body of work both foundational and forward-looking.