  We present a novel application of robust control and online learning for the
balancing of a n Degree of Freedom (DoF), Wheeled Inverted Pendulum (WIP)
humanoid robot. Our technique condenses the inaccuracies of a mass model into a
Center of Mass (CoM) error, balances despite this error, and uses online
learning to update the mass model for a better CoM estimate. Using a simulated
model of our robot, we meta-learn a set of excitory joint poses that makes our
gradient descent algorithm quickly converge to an accurate (CoM) estimate. This
simulated pipeline executes in a fully online fashion, using active disturbance
rejection to address the mass errors that result from a steadily evolving mass
model. Experiments were performed on a 19 DoF WIP, in which we manually
acquired the data for the learned set of poses and show that the mass model
produced by a gradient descent produces a CoM estimate that improves overall
control and efficiency. This work contributes to a greater corpus of whole body
control on the Golem Krang humanoid robot.
