A Novel Training and Collaboration Integrated Framework for Human-Agent Teleoperation

Huang, Zebin and Wang, Ziwei and Bai, Weibang and Huang, Yanpei and Sun, Lichao and Xiao, Bo and Yeatman, Eric M. (2021) A Novel Training and Collaboration Integrated Framework for Human-Agent Teleoperation. Sensors, 21 (24). ISSN 1424-8220

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Abstract

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Item Type:
Journal Article
Journal or Publication Title:
Sensors
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
177045
Deposited By:
Deposited On:
04 Oct 2022 11:15
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 11:54