Learning nonparametric policies by imitation

A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ability to learn by imitation has remained hard to achieve due to a number of factors, including the problem of learning in high-dimensional spaces and the problem of uncertainty. In this paper, we propose a new probabilistic approach to the problem of teaching a high degree-of-freedom robot (in particular, a humanoid robot) flexible and generalizable skills via imitation of a human teacher. The robot uses inference in a graphical model to learn sensor-based dynamics and infer a stable plan from a teacherpsilas demonstration of an action. The novel contribution of this work is a method for learning a nonparametric policy which generalizes a fixed action plan to operate over a continuous space of task variation. A notable feature of the approach is that it does not require any knowledge of the physics of the robot or the environment. By leveraging advances in probabilistic inference and Gaussian process regression, the method produces a nonparametric policy for sensor-based feedback control in continuous state and action spaces. We present experimental and simulation results using a Fujitsu HOAP-2 humanoid robot demonstrating imitation-based learning of a task involving lifting objects of different weights from a single human demonstration.