  We exploit the complementary strengths of vision and proprioception to
develop a point-goal navigation system for legged robots, called VP-Nav. Legged
systems are capable of traversing more complex terrain than wheeled robots, but
to fully utilize this capability, we need a high-level path planner in the
navigation system to be aware of the walking capabilities of the low-level
locomotion policy in varying environments. We achieve this by using
proprioceptive feedback to ensure the safety of the planned path by sensing
unexpected obstacles like glass walls, terrain properties like slipperiness or
softness of the ground and robot properties like extra payload that are likely
missed by vision. The navigation system uses onboard cameras to generate an
occupancy map and a corresponding cost map to reach the goal. A fast marching
planner then generates a target path. A velocity command generator takes this
as input to generate the desired velocity for the walking policy. A safety
advisor module adds sensed unexpected obstacles to the occupancy map and
environment-determined speed limits to the velocity command generator. We show
superior performance compared to wheeled robot baselines, and ablation studies
which have disjoint high-level planning and low-level control. We also show the
real-world deployment of VP-Nav on a quadruped robot with onboard sensors and
computation. Videos at https://navigation-locomotion.github.io
