Skill-level-based Hybrid Shared Control for Human-Automation Systems

In this paper, a hybrid shared controller is proposed for assisting human novice users to emulate human expert users within a human-automation interaction framework. This work is motivated to let human novice users learn the skills of human expert users using automation as a medium. Automation interacts with human users in two folds: it learns how to optimally control the system from the experts demonstrations by offline computation, and assists the novice in real time without excess amount of intervention based on the inference of the novice’s skill-level within our properly designed shared controller. Automation takes more control authority when the novices skill-level is poor, or it allows the novice to have more control authority when his/her skill-level is close to that of the expert to let the novice learn from his/her own control experience. The proposed scheme is shown to be able to improve the system performance while minimizing the intervention from the automation, which is demonstrated via an illustrative human-in-the-loop application example.