  Learning from Hallucination (LfH) is a recent machine learning paradigm for
autonomous navigation, which uses training data collected in completely safe
environments and adds numerous imaginary obstacles to make the environment
densely constrained, to learn navigation planners that produce feasible
navigation even in highly constrained (more dangerous) spaces. However, LfH
requires hallucinating the robot perception during deployment to match with the
hallucinated training data, which creates a need for sometimes-infeasible prior
knowledge and tends to generate very conservative planning. In this work, we
propose a new LfH paradigm that does not require runtime hallucination -- a
feature we call "sober deployment" -- and can therefore adapt to more realistic
navigation scenarios. This novel Hallucinated Learning and Sober Deployment
(HLSD) paradigm is tested in a benchmark testbed of 300 simulated navigation
environments with a wide range of difficulty levels, and in the real-world. In
most cases, HLSD outperforms both the original LfH method and a classical
navigation planner.
