  Humanoid robots are made to resemble humans but their locomotion abilities
are far from ours in terms of agility and versatility. When humans walk on
complex terrains, or face external disturbances, they combine a set of
strategies, unconsciously and efficiently, to regain stability. This paper
tackles the problem of developing a robust omnidirectional walking framework,
which is able to generate versatile and agile locomotion on complex terrains.
The Linear Inverted Pendulum Model and Central Pattern Generator concepts are
used to develop a closed-loop walk engine that is combined with a reinforcement
learning module. This module learns to regulate the walk engine parameters
adaptively and generates residuals to adjust the robot's target joint positions
(residual physics). Additionally, we propose a proximal symmetry loss to
increase the sample efficiency of the Proximal Policy Optimization algorithm by
leveraging model symmetries. The effectiveness of the proposed framework was
demonstrated and evaluated across a set of challenging simulation scenarios.
The robot was able to generalize what it learned in one scenario, by displaying
human-like locomotion skills in unforeseen circumstances, even in the presence
of noise and external pushes.
