  Robots learning from observations in the real world using inverse
reinforcement learning (IRL) may encounter objects or agents in the
environment, other than the expert, that cause nuisance observations during the
demonstration. These confounding elements are typically removed in
fully-controlled environments such as virtual simulations or lab settings. When
complete removal is impossible the nuisance observations must be filtered out.
However, identifying the source of observations when large amounts of
observations are made is difficult. To address this, we present a hierarchical
Bayesian model that incorporates both the expert's and the confounding
elements' observations thereby explicitly modeling the diverse observations a
robot may receive. We extend an existing IRL algorithm originally designed to
work under partial occlusion of the expert to consider the diverse
observations. In a simulated robotic sorting domain containing both occlusion
and confounding elements, we demonstrate the model's effectiveness. In
particular, our technique outperforms several other comparative methods, second
only to having perfect knowledge of the subject's trajectory.
