Probabilistic Imitation Learning in Infants and Robots

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The overall goal of this project is to study learning through imitation and endow robots with the ability to learn in this manner. This cross-disciplinary project has two major goals. The first is to create models for imitation learning in robots that combines techniques from Artificial Intelligence and Bayesian machine learning with insights from cognitive and psychological studies of imitation. The project will use these models to develop a humanoid robot that can learn by watching humans perform specific tasks. The second is to determine what characteristics of a humanoid robot can cause human infants and toddlers to imitate it. This will help shed light on the question of whether infants ascribe intentions to robots. The PI will collaborate with cognitive psychologist, Dr. Andrew Meltzoff also from the University of Washington. The project will foster collaboration between students from both of their labs and provide them with training in carrying out interdisciplinary research.