This paper presents a Mixed-Initiative (MI) framework for addressing the
problem of control authority transfer between a remote human operator and an AI
agent when cooperatively controlling a mobile robot. Our Hierarchical
Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages
information on the human operator's state and intent. The control switching
policies are based on a criticality hierarchy. An experimental evaluation was
conducted in a high-fidelity simulated disaster response and remote inspection
scenario, comparing HierEMICS with a state-of-the-art Expert-guided
Mixed-Initiative Control Switcher (EMICS) in the context of mobile robot
navigation. Results suggest that HierEMICS reduces conflicts for control
between the human and the AI agent, which is a fundamental challenge in both
the MI control paradigm and also in the related shared control paradigm.
Additionally, we provide statistically significant evidence of improved,
navigational safety (i.e., fewer collisions), LOA switching efficiency, and
conflict for control reduction.

本文提出了一种混合倡议 (MI) 框架，用于解决远程人操作员和人工智能代理共同控制移动机器人时控制权转移的问题。我们的 Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) 利用人操作员的状态和意图。结果表明，相比于现有的混合倡议控制切换器 (EMICS)，HierEMICS 减少了人和人工智能代理之间的控制冲突，并提供了卓越的导航安全、LOA 切换效率和控制冲突减少的统计显著证据。