  Many tasks, particularly those involving interaction with the environment,
are characterized by high variability, making robotic autonomy difficult. One
flexible solution is to introduce the input of a human with superior experience
and cognitive abilities as part of a shared autonomy policy. However, current
methods for shared autonomy are not designed to address the wide range of
necessary corrections (e.g., positions, forces, execution rate, etc.) that the
user may need to provide to address task variability. In this paper, we present
corrective shared autonomy, where users provide corrections to key robot state
variables on top of an otherwise autonomous task model. We provide an
instantiation of this shared autonomy paradigm and demonstrate its viability
and benefits such as low user effort and physical demand via a system-level
user study on three tasks involving variability situated in aircraft
manufacturing.
