  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.
