  The musculoskeletal humanoid is difficult to modelize due to the flexibility
and redundancy of its body, whose state can change over time, and so balance
control of its legs is challenging. There are some cases where ordinary PID
controls may cause instability. In this study, to solve these problems, we
propose a method of learning a correlation model among the joint angle, muscle
tension, and muscle length of the ankle and the zero moment point to perform
balance control. In addition, information on the changing body state is
embedded in the model using parametric bias, and the model estimates and adapts
to the current body state by learning this information online. This makes it
possible to adapt to changes in upper body posture that are not directly taken
into account in the model, since it is difficult to learn the complete dynamics
of the whole body considering the amount of data and computation. The model can
also adapt to changes in body state, such as the change in footwear and change
in the joint origin due to recalibration. The effectiveness of this method is
verified by a simulation and by using an actual musculoskeletal humanoid,
Musashi.
