  Imitation learning is one of the methods for reproducing human demonstration
adaptively in robots. So far, it has been found that generalization ability of
the imitation learning enables the robots to perform tasks adaptably in
untrained environments. However, motion styles such as motion trajectory and
the amount of force applied depend largely on the dataset of human
demonstration, and settle down to an average motion style. In this study, we
propose a method that adds parametric bias to the conventional imitation
learning network and can add constraints to the motion style. By experiments
using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is
possible to perform tasks by changing its motion style as intended with
constraints on joint velocity, muscle length velocity, and muscle tension.
