This paper presents a novel approach to modeling curiosity in a
mobile robot, which is useful for monitoring and adaptive data collection
tasks, especially in the context of long term autonomous missions where pre-
programmed missions are likely to have limited utility. We use a realtime topic
modeling technique to build a semantic perception model of the environment,
using which, we plan a path through the locations in the world with high se-
mantic information content. The life-long learning behavior of the proposed
perception model makes it suitable for long-term exploration missions. We val-
idate the approach using simulated exploration experiments using aerial and
underwater data, and demonstrate an implementation on the Aqua underwa-
ter robot in a variety of scenarios. We nd that the proposed exploration paths
that are biased towards locations with high topic perplexity, produce better
terrain models with high discriminative power. Moreover, we show that the
proposed algorithm implemented on Aqua robot is able to do tasks such as
coral reef inspection, diver following, and sea
oor exploration, without any
prior training or preparation.
