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Reward-Based Environment States for Robot Manipulation Policy Learning

2021-12-10 15:55:16
Cédérick Mouliets, Isabelle Ferrané, Heriberto Cuayáhuitl

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.

Abstract (translated)

URL

https://arxiv.org/abs/2112.05621

PDF

https://arxiv.org/pdf/2112.05621.pdf


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