  To operate in a real-world environment, robots have several requirements
including environmental adaptability. Moreover, the desired success rate for
the completion of tasks must be achieved. In this regard, end-to-end learning
for autonomous operation is currently being investigated. However, the issue of
operating speed has not been investigated in detail. Therefore, in this paper,
we propose a method for generating variable operating speeds while adapting to
perturbations in the environment. When the work speed changes, there is a
nonlinear relationship between the operating speed and force (e.g., inertial
and frictional forces). However, the proposed method can be adapted to
nonlinearities by utilizing minimal motion data. We experimentally evaluated
the proposed method for erasing a line using an eraser fixed to the tip of a
robot. Furthermore, the proposed method enables a robot to perform a task
faster than a human operator.
