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 teacher’s 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 r...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: David B. Grimes, Rajesh P. N. Rao
• Keywords: Human Teacher, Humanoid Robot, IROS 2008, Robot, Robotics