An official website of the United States government
In this paper, we propose a human-automation interaction scheme to improve the task performance of novice human users with different skill levels. The proposed scheme includes two interaction modes: learn from experts mode and assist novices mode. In the learn from experts mode, the automation learns from a human expert user such that the awareness of task objective is obtained. Based on the learned task objective, in the assist novices mode, the automation customizes its control parameter to assist a novice human user towards emulating the performance of the expert human user. We experimentally test the proposed human-automation scheme in a designed quadrotor simulation environment, and the results show that the proposed approach is capable of adapting to and assisting the novice human user to achieve the performance that emulates the expert human user.
more »
« less
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.
more »
« less
- Award ID(s):
- 1836952
- PAR ID:
- 10343807
- Date Published:
- Journal Name:
- IEEE International Conference on Systems, Man, and Cybernetics
- Page Range / eLocation ID:
- 1507 to 1512
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
null (Ed.)In this paper, we argue in favor of creating robots that both teach and learn. We propose a methodology for building robots that can learn a skill from an expert, perform the skill independently or collaboratively with the expert, and then teach the same skill to a novice. This requires combining insights from learning from demonstration, human-robot collaboration, and intelligent tutoring systems to develop knowledge representations that can be shared across all three components. As a case study for our methodology, we developed a glockenspiel-playing robot. The robot begins as a novice, learns how to play musical harmonies from an expert, collaborates with the expert to complete harmonies, and then teaches the harmonies to novice users. This methodology allows for new evaluation metrics that provide a thorough understanding of how well the robot has learned and enables a robot to act as an efficient facilitator for teaching across temporal and geographic separation.more » « less
-
Shared control in semi-automated vehicles requires dynamic, bidirectional arbitration between human and automation. A central challenge is maintaining safety while reducing unnecessary opposition despite uncertainty in driver intent and interaction dynamics. This paper presents a learning-based predictive arbitration framework that integrates Gaussian process regression with multistage model predictive control. Human-in-the-loop driving simulator data are used to learn residual mismatch between predicted and observed shared-control behavior. The learned probabilistic model is embedded within a scenario-based MPC to adapt automation impedance and authority online. Closed-loop obstacle-avoidance simulations show that learning resolves conflict more effectively than a worst-case baseline by preserving safety, reducing conservatism, opposing the human less, and producing smoother, more cooperative steering interactions. Keywords:more » « less
-
We propose a novel criterion for evaluating user input for human-robot interfaces for known tasks. We use the mode insertion gradient (MIG)—a tool from hybrid control theory—as a filtering criterion that instantaneously assesses the impact of user actions on a dynamic system over a time window into the future. As a result, the filter is permissive to many chosen strategies, minimally engaging, and skill-sensitive—qualities desired when evaluating human actions. Through a human study with 28 healthy volunteers, we show that the criterion exhibits a low, but significant, negative correlation between skill level, as estimated from task-specific measures in unassisted trials, and the rate of controller intervention during assistance. Moreover, a MIG-based filter can be utilized to create a shared control scheme for training or assistance. In the human study, we observe a substantial training effect when using a MIG-based filter to perform cart-pendulum inversion, particularly when comparing improvement via the RMS error measure. Using simulation of a controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe that the MIG criterion could be used for assistance to guarantee either task completion or safety of a joint human-robot system, while maintaining the system’s flexibility with respect to user-chosen strategies.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/SMC52423.2021.9658994
