  Recently, motion generation by machine learning has been actively researched
to automate various tasks. Imitation learning is one such method that learns
motions from data collected in advance. However, executing long-term tasks
remains challenging. Therefore, a novel framework for imitation learning is
proposed to solve this problem. The proposed framework comprises upper and
lower layers, where the upper layer model, whose timescale is long, and lower
layer model, whose timescale is short, can be independently trained. In this
model, the upper layer learns long-term task planning, and the lower layer
learns motion primitives. The proposed method was experimentally compared to
hierarchical RNN-based methods to validate its effectiveness. Consequently, the
proposed method showed a success rate equal to or greater than that of
conventional methods. In addition, the proposed method required less than 1/20
of the training time compared to conventional methods. Moreover, it succeeded
in executing unlearned tasks by reusing the trained lower layer.
