  We present a novel deep neural network architecture for representing robot
experiences in an episodic-like memory which facilitates encoding, recalling,
and predicting action experiences. Our proposed unsupervised deep episodic
memory model 1) encodes observed actions in a latent vector space and, based on
this latent encoding, 2) infers most similar episodes previously experienced,
3) reconstructs original episodes, and 4) predicts future frames in an
end-to-end fashion. Results show that conceptually similar actions are mapped
into the same region of the latent vector space. Based on these results, we
introduce an action matching and retrieval mechanism, benchmark its performance
on two large-scale action datasets, 20BN-something-something and ActivityNet
and evaluate its generalization capability in a real-world scenario on a
humanoid robot.
