  The generation of robot motions in the real world is difficult by using
conventional controllers alone and requires highly intelligent processing. In
this regard, learning-based motion generations are currently being
investigated. However, the main issue has been improvements of the adaptability
to spatially varying environments, but a variation of the operating speed has
not been investigated in detail. In contact-rich tasks, it is especially
important to be able to adjust the operating speed because a nonlinear
relationship occurs between the operating speed and force (e.g., inertial and
frictional forces), and it affects the results of the tasks. Therefore, in this
study, we propose a method for generating variable operating speeds while
adapting to spatial perturbations in the environment. The proposed method can
be adapted to nonlinearities by utilizing a small amount of motion data. We
experimentally evaluated the proposed method by erasing a line using an eraser
fixed to the tip of the robot as an example of a contact-rich task.
Furthermore, the proposed method enables a robot to perform a task faster than
a human operator and is capable of operating close to the control bandwidth.
