  Outdoor navigation on sidewalks in urban environments is the key technology
behind important human assistive applications, such as last-mile delivery or
neighborhood patrol. This paper aims to develop a quadruped robot that follows
a route plan generated by public map services, while remaining on sidewalks and
avoiding collisions with obstacles and pedestrians. We devise a two-staged
learning framework, which first trains a teacher agent in an abstract world
with privileged ground-truth information, and then applies Behavior Cloning to
teach the skills to a student agent who only has access to realistic sensors.
The main research effort of this paper focuses on overcoming challenges when
deploying the student policy on a quadruped robot in the real world. We propose
methodologies for designing sensing modalities, network architectures, and
training procedures to enable zero-shot policy transfer to unstructured and
dynamic real outdoor environments. We evaluate our learning framework on a
quadrupedal robot navigating sidewalks in the city of Atlanta, USA. Using the
learned navigation policy and its onboard sensors, the robot is able to walk
3.2 kilometers with a limited number of human interventions.
