  Scarce data is a major challenge to scaling robot learning to truly complex
tasks, as we need to generalize locally learned policies over different task
contexts. Contextual policy search offers data-efficient learning and
generalization by explicitly conditioning the policy on a parametric context
space. In this paper, we further structure the contextual policy
representation. We propose to factor contexts into two components: target
contexts that describe the task objectives, e.g. target position for throwing a
ball; and environment contexts that characterize the environment, e.g. initial
position or mass of the ball. Our key observation is that experience can be
directly generalized over target contexts. We show that this can be easily
exploited in contextual policy search algorithms. In particular, we apply
factorization to a Bayesian optimization approach to contextual policy search
both in sampling-based and active learning settings. Our simulation results
show faster learning and better generalization in various robotic domains. See
our supplementary video: https://youtu.be/MNTbBAOufDY.
