  Robot motion generation methods using machine learning have been studied in
recent years. Bilateral controlbased imitation learning can imitate human
motions using force information. By means of this method, variable speed motion
generation that considers physical phenomena such as the inertial force and
friction can be achieved. Previous research demonstrated that the complex
relationship between the force and speed can be learned by using a neural
network model. However, the previous study only focused on a simple
reciprocating motion. To learn the complex relationship between the force and
speed more accurately, it is necessary to learn multiple actions using many
joints. In this paper, we propose a variable speed motion generation method for
multiple motions. We considered four types of neural network models for the
motion generation and determined the best model for multiple motions at
variable speeds. Subsequently, we used the best model to evaluate the
reproducibility of the task completion time for the input completion time
command. The results revealed that the proposed method could change the task
completion time according to the specified completion time command in multiple
motions.
