  Model-based curiosity combines active learning approaches to optimal sampling
with the information gain based incentives for exploration presented in the
curiosity literature. Existing model-based curiosity methods look to
approximate prediction uncertainty with approaches which struggle to scale to
many prediction-planning pipelines used in robotics tasks. We address these
scalability issues with an adversarial curiosity method minimizing a score
given by a discriminator network. This discriminator is optimized jointly with
a prediction model and enables our active learning approach to sample sequences
of observations and actions which result in predictions considered the least
realistic by the discriminator. We demonstrate progressively increasing
advantages as compute is restricted of our adversarial curiosity approach over
leading model-based exploration strategies in simulated environments. We
further demonstrate the ability of our adversarial curiosity method to scale to
a robotic manipulation prediction-planning pipeline where we improve sample
efficiency and prediction performance for a domain transfer problem.
