  We tackle the problem of perceptive locomotion in dynamic environments. In
this problem, a quadrupedal robot must exhibit robust and agile walking
behaviors in response to environmental clutter and moving obstacles. We present
a hierarchical learning framework, named PRELUDE, which decomposes the problem
of perceptive locomotion into high-level decision-making to predict navigation
commands and low-level gait generation to realize the target commands. In this
framework, we train the high-level navigation controller with imitation
learning on human demonstrations collected on a steerable cart and the
low-level gait controller with reinforcement learning (RL). Therefore, our
method can acquire complex navigation behaviors from human supervision and
discover versatile gaits from trial and error. We demonstrate the effectiveness
of our approach in simulation and with hardware experiments. Videos and code
can be found at the project page: https://ut-austin-rpl.github.io/PRELUDE.
