Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from
  Learned Hallucination
Saad Abdul Ghani Zizhao Wang Peter Stone Xuesu Xiao
This paper presents a self-supervised learning method to safely learn a
motion planner for ground robots to navigate environments with dense and
dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict
obstacles, classical motion planners may not be able to keep up with limited
onboard computation. For learning-based planners, high-quality demonstrations
are difficult to acquire for imitation learning while reinforcement learning
becomes inefficient due to the high probability of collision during
exploration. To safely and efficiently provide training data, the Learning from
Hallucination (LfH) approaches synthesize difficult navigation environments
based on past successful navigation experiences in relatively easy or
completely open ones, but unfortunately cannot address dynamic obstacles. In
our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and
learn a novel latent distribution and sample dynamic obstacles from it, so the
generated training data can be used to learn a motion planner to navigate in
dynamic environments. Dyna-LfLH is evaluated on a ground robot in both
simulated and physical environments and achieves up to 25% better success rate
compared to baselines.