  The flexible musculoskeletal hand is difficult to modelize, and its model can
change constantly due to deterioration over time, irreproducibility of
initialization, etc. Also, for object recognition, contact detection, and
contact control using the hand, it is desirable not to use a neural network
trained for each task, but to use only one integrated network. Therefore, we
develop a method to acquire a sensor state equation of the musculoskeletal hand
using a recurrent neural network with parametric bias. By using this network,
the hand can realize recognition of the grasped object, contact simulation,
detection, and control, and can cope with deterioration over time,
irreproducibility of initialization, etc. by updating parametric bias. We apply
this study to the hand of the musculoskeletal humanoid Musashi and show its
effectiveness.
