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Robot Manipulation Learning Using Generative Adversarial Imitation Learning

Abstract : Imitation learning allows learning complex behaviors given demonstrations. Early approaches belonging to either Behavior Cloning or Inverse Reinforcement Learning were however of limited scalability to complex environments. A more promising approach termed as Generative Adversarial Imitation Learning tackles the imitation learning problem by drawing a connection with Generative Adversarial Networks. In this work, we advocate the use of this class of methods and investigate possible extensions by endowing them with global temporal consistency, in particular through a contrastive learning based approach.
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Contributor : Mohamed Khalil Jabri Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 6:12:15 PM
Last modification on : Monday, October 11, 2021 - 2:24:03 PM


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Mohamed Khalil Jabri. Robot Manipulation Learning Using Generative Adversarial Imitation Learning. Thirtieth International Joint Conference on Artificial Intelligence, Aug 2021, Montreal (virtual), Canada. pp.4893-4894, ⟨10.24963/ijcai.2021/678⟩. ⟨hal-03352265⟩



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