  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 semantic information content.
The life-long learning behavior of the proposed perception model makes it
suitable for long-term exploration missions. We validate the approach using
simulated exploration experiments using aerial and underwater data, and
demonstrate an implementation on the Aqua underwater robot in a variety of
scenarios. We find 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 floor exploration, without any prior training or
preparation.
