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.
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Stochastic-Skill-Level-Based Shared Control for Human Training in Urban Air Mobility Scenario
This paper proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable which denotes the probability that the novice will behave similarly to experts. We propose a data-driven method which can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility (UAM) vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real-time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework.
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- Award ID(s):
- 1836952
- PAR ID:
- 10519273
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Human-Robot Interaction
- ISSN:
- 2573-9522
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3603194
