Online Center of Mass Estimation for a Humanoid Wheeled Inverted Pendulum Robot
This work addresses balancing challenges for humanoid robots, but it is incremental as it builds on existing control and learning methods for a specific robot platform.
The authors tackled the problem of balancing a wheeled inverted pendulum humanoid robot by estimating its center of mass online, using robust control and online learning to improve accuracy and efficiency in simulations and experiments on a 19-degree-of-freedom robot.
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.