  Partial Automation (PA) with intelligent support systems has been introduced
in industrial machinery and advanced automobiles to reduce the burden of long
hours of human operation. Under PA, operators perform manual operations
(providing actions) and operations that switch to automatic/manual mode
(mode-switching). Since PA reduces the total duration of manual operation,
these two action and mode-switching operations can be replicated by imitation
learning with high sample efficiency. To this end, this paper proposes
Disturbance Injection under Partial Automation (DIPA) as a novel imitation
learning framework. In DIPA, mode and actions (in the manual mode) are assumed
to be observables in each state and are used to learn both action and
mode-switching policies. The above learning is robustified by injecting
disturbances into the operator's actions to optimize the disturbance's level
for minimizing the covariate shift under PA. We experimentally validated the
effectiveness of our method for long-horizon tasks in two simulations and a
real robot environment and confirmed that our method outperformed the previous
methods and reduced the demonstration burden.
