  Legged robots are increasingly entering new domains and applications,
including search and rescue, inspection, and logistics. However, for such
systems to be valuable in real-world scenarios, they must be able to
autonomously and robustly navigate irregular terrains. In many cases, robots
that are sold on the market do not provide such abilities, being able to
perform only blind locomotion. Furthermore, their controller cannot be easily
modified by the end-user, requiring a new and time-consuming control synthesis.
In this work, we present a fast local motion planning pipeline that extends the
capabilities of a black-box walking controller that is only able to track
high-level reference velocities. More precisely, we learn a set of motion
models for such a controller that maps high-level velocity commands to Center
of Mass (CoM) and footstep motions. We then integrate these models with a
variant of the A star algorithm to plan the CoM trajectory, footstep sequences,
and corresponding high-level velocity commands based on visual information,
allowing the quadruped to safely traverse irregular terrains at demand.
