@article{8ae3c4d3916444b98a062ff08fdb7f4a,
title = "Skill Training for Space Teleoperation: DRL-Based Intelligent Agent for Training Strategy Adjustment With Innovative Evaluation",
abstract = "This",
keywords = "DRL-based intelligent agent, Dual-user shared control, Fr{\^A}' echet-Distance, Skill training, Space teleoperation",
author = "Yang Yang and Panfeng Huang and Haifei Chen and Xing Liu",
note = "Publisher Copyright: {\textcopyright}chet-distance. Operating within a dual-user shared control framework, the system balances practical task engagement with essential task safety. Departing from traditional expert-dependent and manually crafted training strategies, the study employs deep reinforcement learning (DRL) controlled by intelligent agents. The DRL method transforms the trainee engagement and operational guidance calibration into a sophisticated multiobjective optimization problem. This transformation facilitates the tailored refinement of training strategies in sync with each trainee's skill level, leading to the amplification of training effectiveness. Moreover, the system includes a task-independent skill assessment mechanism, employing the Fr{\~A}",
year = "2026",
month = may,
day = "1",
doi = "10.1109/MAES.2025.3628220",
language = "英语",
volume = "41",
pages = "84--100",
journal = "IEEE Aerospace and Electronic Systems Magazine",
issn = "0885-8985",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",
}