  The tendon-driven musculoskeletal humanoid has many benefits that human
beings have, but the modeling of its complex muscle and bone structures is
difficult and conventional model-based controls cannot realize intended
movements. Therefore, a learning control mechanism that acquires nonlinear
relationships between joint angles, muscle tensions, and muscle lengths from
the actual robot is necessary. In this study, we propose a system which runs
the learning control mechanism for a long time to keep the self-body image of
the musculoskeletal humanoid correct at all times. Also, we show that the
musculoskeletal humanoid can conduct position control, torque control, and
variable stiffness control using this self-body image. We conduct a long-time
self-body image acquisition experiment lasting 3 hours, evaluate variable
stiffness control using the self-body image, etc., and discuss the superiority
and practicality of the self-body image acquisition of musculoskeletal
structures, comprehensively.
