  Discontinuous motion which is a motion composed of multiple continuous
motions with sudden change in direction or velocity in between, can be seen in
state-aware robotic tasks. Such robotic tasks are often coordinated with sensor
information such as image. In recent years, Dynamic Movement Primitives (DMP)
which is a method for generating motor behaviors suitable for robotics has
garnered several deep learning based improvements to allow associations between
sensor information and DMP parameters. While the implementation of deep
learning framework does improve upon DMP's inability to directly associate to
an input, we found that it has difficulty learning DMP parameters for complex
motion which requires large number of basis functions to reconstruct. In this
paper we propose a novel deep learning network architecture called Deep
Segmented DMP Network (DSDNet) which generates variable-length segmented motion
by utilizing the combination of multiple DMP parameters predicting network
architecture, double-stage decoder network, and number of segments predictor.
The proposed method is evaluated on both artificial data (object cutting &
pick-and-place) and real data (object cutting) where our proposed method could
achieve high generalization capability, task-achievement, and data-efficiency
compared to previous method on generating discontinuous long-horizon motions.
