  In this paper, with a view toward deployment of light-weight control
frameworks for bipedal walking robots, we realize end-foot trajectories that
are shaped by a single linear feedback policy. We learn this policy via a
model-free and a gradient-free learning algorithm, Augmented Random Search
(ARS), in the two robot platforms Rabbit and Digit. Our contributions are
two-fold: a) By using torso and support plane orientation as inputs, we achieve
robust walking on slopes of up to 20 degrees in simulation. b) We demonstrate
additional behaviors like walking backwards, stepping-in-place, and recovery
from external pushes of up to 120 N. The end result is a robust and a fast
feedback control law for bipedal walking on terrains with varying slopes.
Towards the end, we also provide preliminary results of hardware transfer to
Digit.
