  While classical approaches to autonomous robot navigation currently enable
operation in certain environments, they break down in tightly constrained
spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze
between obstacles. Recent machine learning techniques have the potential to
address this shortcoming, but existing approaches require vast amounts of
navigation experience for training, during which the robot must operate in
close proximity to obstacles and risk collision. In this paper, we propose to
side-step this requirement by introducing a new machine learning paradigm for
autonomous navigation called learning from hallucination (LfH), which can use
training data collected in completely safe environments to compute navigation
controllers that result in fast, smooth, and safe navigation in highly
constrained environments. Our experimental results show that the proposed LfH
system outperforms three autonomous navigation baselines on a real robot and
generalizes well to unseen environments, including those based on both
classical and machine learning techniques.
