  While the musculoskeletal humanoid has various biomimetic benefits, the
modeling of its complex structure is difficult, and many learning-based systems
have been developed so far. There are various methods, such as control methods
using acquired relationships between joints and muscles represented by a data
table or neural network, and state estimation methods using Extended Kalman
Filter or table search. In this study, we construct a Musculoskeletal
AutoEncoder representing the relationship among joint angles, muscle tensions,
and muscle lengths, and propose a unified method of state estimation, control,
and simulation of musculoskeletal humanoids using it. By updating the
Musculoskeletal AutoEncoder online using the actual robot sensor information,
we can continuously conduct more accurate state estimation, control, and
simulation than before the online learning. We conducted several experiments
using the musculoskeletal humanoid Musashi, and verified the effectiveness of
this study.
