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
-
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
-
The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMC52423.2021.9658994
