  We present Visual Navigation and Locomotion over obstacles (ViNL), which
enables a quadrupedal robot to navigate unseen apartments while stepping over
small obstacles that lie in its path (e.g., shoes, toys, cables), similar to
how humans and pets lift their feet over objects as they walk. ViNL consists
of: (1) a visual navigation policy that outputs linear and angular velocity
commands that guides the robot to a goal coordinate in unfamiliar indoor
environments; and (2) a visual locomotion policy that controls the robot's
joints to avoid stepping on obstacles while following provided velocity
commands. Both the policies are entirely "model-free", i.e. sensors-to-actions
neural networks trained end-to-end. The two are trained independently in two
entirely different simulators and then seamlessly co-deployed by feeding the
velocity commands from the navigator to the locomotor, entirely "zero-shot"
(without any co-training). While prior works have developed learning methods
for visual navigation or visual locomotion, to the best of our knowledge, this
is the first fully learned approach that leverages vision to accomplish both
(1) intelligent navigation in new environments, and (2) intelligent visual
locomotion that aims to traverse cluttered environments without disrupting
obstacles. On the task of navigation to distant goals in unknown environments,
ViNL using just egocentric vision significantly outperforms prior work on
robust locomotion using privileged terrain maps (+32.8% success and -4.42
collisions per meter). Additionally, we ablate our locomotion policy to show
that each aspect of our approach helps reduce obstacle collisions. Videos and
code at http://www.joannetruong.com/projects/vinl.html
