Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

NeurIPS 2017 Karol HausmanYevgen ChebotarStefan SchaalGaurav SukhatmeJoseph Lim

Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks... (read more)

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