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About

Andreea Bobu is a leading researcher in human-robot interaction, focusing on how robots can learn from and align with human preferences, representations, and emotions. Her work tackles the fundamental challenge of enabling robots to understand not just *what* humans want, but *how* they perceive and value the world. A core contribution is her development of methods that allow robots to learn task-relevant features and concepts directly from human input—such as corrections, queries, or language—rather than relying on pre-defined, handcrafted features. Her highly cited paper, "Inducing structure in reward learning by learning features" (2022, 20 citations), addresses the critical problem of misspecified objective spaces, where a robot's assumptions about what matters in a task are incomplete. Bobu also explores the expressive and social dimensions of robotics, teaching robots to perform functional tasks with emotive qualities that reflect human emotional states or confidence levels. Her work on aligning human and robot representations (2024, 16 citations) is foundational for building robots that can truly collaborate with people. With over 80 citations across her key papers, Bobu is shaping the future of adaptable, human-aware robotics, making her research essential for anyone interested in creating robots that learn and communicate naturally with people.