  In this work, we demonstrate robust walking in the bipedal robot Digit on
uneven terrains by just learning a single linear policy. In particular, we
propose a new control pipeline, wherein the high-level trajectory modulator
shapes the end-foot ellipsoidal trajectories, and the low-level gait controller
regulates the torso and ankle orientation. The foot-trajectory modulator uses a
linear policy and the regulator uses a linear PD control law. As opposed to
neural network-based policies, the proposed linear policy has only 13 learnable
parameters, thereby not only guaranteeing sample efficient learning but also
enabling simplicity and interpretability of the policy. This is achieved with
no loss of performance on challenging terrains like slopes, stairs and outdoor
landscapes. We first demonstrate robust walking in the custom simulation
environment, MuJoCo, and then directly transfer to hardware with no
modification of the control pipeline. We subject the biped to a series of
pushes and terrain height changes, both indoors and outdoors, thereby
validating the presented work.
