  This paper introduces a novel Learning from Demonstration framework to learn
robotic skills with keyframe demonstrations using a Dynamic Bayesian Network
(DBN) and a Bayesian Optimized Policy Search approach to improve the learned
skills. DBN learns the robot motion, perceptual change in the object of
interest (aka skill sub-goals) and the relation between them. The rewards are
also learned from the perceptual part of the DBN. The policy search part is a
semiblack box algorithm, which we call BO-PI2 . It utilizes the
action-perception relation to focus the high-level exploration, uses Gaussian
Processes to model the expected-return and performs Upper Confidence Bound type
low-level exploration for sampling the rollouts. BO-PI2 is compared against a
stateof-the-art method on three different skills in a real robot setting with
expert and naive user demonstrations. The results show that our approach
successfully focuses the exploration on the failed sub-goals and the addition
of reward-predictive exploration outperforms the state-of-the-art approach on
cumulative reward, skill success, and termination time metrics.
