  Recent work has shown results on learning navigation policies for idealized
cylinder agents in simulation and transferring them to real wheeled robots.
Deploying such navigation policies on legged robots can be challenging due to
their complex dynamics, and the large dynamical difference between cylinder
agents and legged systems. In this work, we learn hierarchical navigation
policies that account for the low-level dynamics of legged robots, such as
maximum speed, slipping, contacts, and learn to successfully navigate cluttered
indoor environments. To enable transfer of policies learned in simulation to
new legged robots and hardware, we learn dynamics-aware navigation policies
across multiple robots with robot-specific embeddings. The learned embedding is
optimized on new robots, while the rest of the policy is kept fixed, allowing
for quick adaptation. We train our policies across three legged robots in
simulation - 2 quadrupeds (A1, AlienGo) and a hexapod (Daisy). At test time, we
study the performance of our learned policy on two new legged robots in
simulation (Laikago, 4-legged Daisy), and one real-world quadrupedal robot
(A1). Our experiments show that our learned policy can sample-efficiently
generalize to previously unseen robots, and enable sim-to-real transfer of
navigation policies for legged robots.
