  In this work, we present a learning-based pipeline to realise local
navigation with a quadrupedal robot in cluttered environments with static and
dynamic obstacles. Given high-level navigation commands, the robot is able to
safely locomote to a target location based on frames from a depth camera
without any explicit mapping of the environment. First, the sequence of images
and the current trajectory of the camera are fused to form a model of the world
using state representation learning. The output of this lightweight module is
then directly fed into a target-reaching and obstacle-avoiding policy trained
with reinforcement learning. We show that decoupling the pipeline into these
components results in a sample efficient policy learning stage that can be
fully trained in simulation in just a dozen minutes. The key part is the state
representation, which is trained to not only estimate the hidden state of the
world in an unsupervised fashion, but also helps bridging the reality gap,
enabling successful sim-to-real transfer. In our experiments with the
quadrupedal robot ANYmal in simulation and in reality, we show that our system
can handle noisy depth images, avoid dynamic obstacles unseen during training,
and is endowed with local spatial awareness.
