  Training robot manipulation policies is a challenging and open problem in
robotics and artificial intelligence. In this paper we propose a novel and
compact state representation based on the rewards predicted from an image-based
task success classifier. Our experiments, using the Pepper robot in simulation
with two deep reinforcement learning algorithms on a grab-and-lift task, reveal
that our proposed state representation can achieve up to 97% task success using
our best policies.
