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

Reward-Based Environment States for Robot Manipulation Policy Learning