  This paper presents a self-supervised Learning from Learned Hallucination
(LfLH) method to learn fast and reactive motion planners for ground and aerial
robots to navigate through highly constrained environments. The recent Learning
from Hallucination (LfH) paradigm for autonomous navigation executes motion
plans by random exploration in completely safe obstacle-free spaces, uses
hand-crafted hallucination techniques to add imaginary obstacles to the robot's
perception, and then learns motion planners to navigate in realistic,
highly-constrained, dangerous spaces. However, current hand-crafted
hallucination techniques need to be tailored for specific robot types (e.g., a
differential drive ground vehicle), and use approximations heavily dependent on
certain assumptions (e.g., a short planning horizon). In this work, instead of
manually designing hallucination functions, LfLH learns to hallucinate obstacle
configurations, where the motion plans from random exploration in open space
are optimal, in a self-supervised manner. LfLH is robust to different robot
types and does not make assumptions about the planning horizon. Evaluated in
both simulated and physical environments with a ground and an aerial robot,
LfLH outperforms or performs comparably to previous hallucination approaches,
along with sampling- and optimization-based classical methods.
