  Various adaptive abilities are required for robots interacting with humans in
daily life. It is difficult to design adaptive algorithms manually; however, by
using end-to-end machine learning, labor can be saved during the design
process. In our previous research, a task requiring force adjustment was
achieved through imitation learning that considered position and force
information using a four-channel bilateral control. Unfortunately, tasks that
include long-term (slow) motion are still challenging. Furthermore, during
system identification, there is a method known as the multi-decimation (MD)
identification method. It separates lower and higher frequencies, and then
identifies the parameters characterized at each frequency. Therefore, we
proposed utilizing machine learning to take advantage of the MD method to infer
short-term and long-term (high and low frequency, respectively) motion. In this
paper, long-term motion tasks such as writing a letter using a pen fixed on a
robot are discussed. We found differences in suitable sampling periods between
position and force information. The validity of the proposed method was then
experimentally verified, showing the importance of long-term inference with
adequate sampling periods.
