  In humans, intrinsic motivation is an important mechanism for open-ended
cognitive development; in robots, it has been shown to be valuable for
exploration. An important aspect of human cognitive development is
$\textit{episodic memory}$ which enables both the recollection of events from
the past and the projection of subjective future. This paper explores the use
of visual episodic memory as a source of intrinsic motivation for robotic
exploration problems. Using a convolutional recurrent neural network
autoencoder, the agent learns an efficient representation for spatiotemporal
features such that accurate sequence prediction can only happen once
spatiotemporal features have been learned. Structural similarity between ground
truth and autoencoder generated images is used as an intrinsic motivation
signal to guide exploration. Our proposed episodic memory model also implicitly
accounts for the agent's actions, motivating the robot to seek new interactive
experiences rather than just areas that are visually dissimilar. When guiding
robotic exploration, our proposed method outperforms the Curiosity-driven
Variational Autoencoder (CVAE) at finding dynamic anomalies.
