An adaptive probabilistic approach to goal-level imitation learning

Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence is available). A GHDBN, once trained, is able to recognize skills being observed and to reproduce them by exploiting the generative power of the model. The system has been successfully tested in simulation, and initial tests have been conducted on a NAO humanoid robot platform.