Humans are extremely adept at learning new skills by watching and imitating others. Attempts to endow robots with a similar ability have failed to generalize beyond specific tasks, partly because the focus has been on following the trajectory of an action demonstrated by an expert.
The current project investigates a new interdisciplinary approach to imitation learning that is inspired by how humans learn via goal-based imitation. The project's specific objectives include: (1) a new method for imitation based on inferring the underlying goals of human actions rather than following trajectories: actions are executed based on sequences of inferred goals and successfully executed action sequences are cached as higher level goals, leading to hierarchical goal-based imitation; (2) a new approach based on hierarchical Bayesian models (HBMs) is proposed for generalization across objects and tasks, and (3) developmental studies of goal-based imitation learning are proposed for testing predictions of the project?s models in imitation learning experiments with children.
The project represents one of the first efforts to develop rigorous probabilistic models of goal-based imitation learning based on insights from human learning. The results are expected to pave the way for a new generation of machines that can interact fluently with humans, learn new skills from human teachers, and cooperatively solve problems with human partners. The project also provides graduate and undergraduate students with multidisciplinary training in computer science and cognitive science, with K-12 outreach activities aimed at encouraging students from underrepresented groups to pursue careers in science and engineering.
