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arXiv · 1903.10304

Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors

Fang-I Hsiao·Jui-Hsuan Kuo·Min Sun
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Abstract

We propose a novel approach to train a multi-modal policy from mixed demonstrations without their behavior labels. We develop a method to discover the latent factors of variation in the demonstrations. Specifically, our method is based on the variational autoencoder with a categorical latent variable. The encoder infers discrete latent factors corresponding to different behaviors from demonstrations. The decoder, as a policy, performs the behaviors accordingly. Once learned, the policy is able to reproduce a specific behavior by simply conditioning on a categorical vector. We evaluate our method on three different tasks, including a challenging task with high-dimensional visual inputs. Experimental results show that our approach is better than various baseline methods and competitive with a multi-modal policy trained by ground truth behavior labels.

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Fang-I Hsiao, Jui-Hsuan Kuo, Min Sun. 2019-03-25. Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors. https://arxiv.org/abs/1903.10304

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