  Informative planning seeks a sequence of actions that guide the robot to
collect the most informative data to build a large-scale environmental model or
learn a dynamical system. Existing work in informative planning mainly focuses
on proposing new planners and applying them to various robotic applications
such as environmental monitoring, autonomous exploration, and system
identification. The informative planners optimize an objective given by a
probabilistic model, e.g., Gaussian process regression (GPR). In practice, the
ubiquitous sensing outliers can easily affect the model, resulting in a
misleading objective. A straightforward solution is to filter out the outliers
in the sensing data stream using an off-the-shelf outlier detector. However,
informative samples are also scarce by definition so they might be falsely
filtered out. In this paper, we propose a method to enable the robot to
re-visit the locations where outliers were sampled besides optimizing the
informative planning objective. The robot can collect more samples in the
vicinity of outliers and update the outlier detector to reduce the number of
false alarms. We achieve this by designing a new objective for the Pareto Monte
Carlo tree search (MCTS). We demonstrate that the proposed framework performs
better than applying an outlier detector naively.
