Cédérick, Mouliets, Ferrané, Isabelle and Cuayahuitl, Heriberto (2021) Reward-Based Environment States for Robot Manipulation Policy Learning. In: NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM).
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cm_NeurIPS2021.pdf - Whole Document 298kB |
| Item Type: | Conference or Workshop contribution (Poster) |
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| Item Status: | Live Archive |
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.
| Keywords: | robot manipulation, deep reinforcement learning |
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| Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G760 Machine Learning H Engineering > H671 Robotics |
| Divisions: | College of Science > School of Computer Science |
| ID Code: | 47522 |
| Deposited On: | 20 Jan 2022 15:10 |
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