Poster
in
Workshop: Deployable Decision Making in Embodied Systems (DDM)

Reward-Based Environment States for Robot Manipulation Policy Learning

Isabelle Ferrane · Heriberto Cuayahuitl


Abstract:

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.

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