Corrective Shared Autonomy for Addressing Task Variability
This addresses the challenge of task variability in robotics for domains like manufacturing, but it is incremental as it builds on existing shared autonomy methods.
The paper tackles the problem of high task variability in robotics by introducing corrective shared autonomy, where users correct key robot state variables on top of an autonomous model, and demonstrates its viability with benefits like low user effort and physical demand in a user study on aircraft manufacturing tasks.
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.