  Tendon-driven musculoskeletal humanoids have many benefits in terms of the
flexible spine, multiple degrees of freedom, and variable stiffness. At the
same time, because of its body complexity, there are problems in
controllability. First, due to the large difference between the actual robot
and its geometric model, it cannot move as intended and large internal muscle
tension may emerge. Second, movements which do not appear as changes in muscle
lengths may emerge, because of the muscle route changes caused by softness of
body tissue. To solve these problems, we construct two models: ideal
joint-muscle model and muscle-route change model, using a neural network. We
initialize these models by a man-made geometric model and update them online
using the sensor information of the actual robot. We validate that the
tendon-driven musculoskeletal humanoid Kengoro is able to obtain a correct
self-body image through several experiments.
