Using different Levels of Autonomy (LoA), a human operator can vary the extent of control they have over a robot's actions. LoAs enable operators to mitigate a robot's performance degradation or limitations in the its autonomous capabilities. However, LoA regulation and other tasks may often overload an operator's cognitive abilities. Inspired by video game user interfaces, we study if adding a 'Robot Health Bar' to the robot control UI can reduce the cognitive demand and perceptual effort required for LoA regulation while promoting trust and transparency. This Health Bar uses the robot vitals and robot health framework to quantify and present runtime performance degradation in robots. Results from our pilot study indicate that when using a health bar, operators used to manual control more to minimise the risk of robot failure during high performance degradation. It also gave us insights and lessons to inform subsequent experiments on human-robot teaming.

翻译：不同自主等级允许人类操作者调节其对机器人行为的控制程度。自主等级使操作者能够应对机器人性能退化或自主能力的局限性。然而，自主等级调节及其他任务常会使操作者的认知负荷过重。受视频游戏用户界面启发，我们研究在机器人控制界面添加“机器人健康条”能否降低自主等级调节所需的认知需求和感知努力，同时提升信任度与透明度。该健康条基于机器人生命体征与机器人健康框架，对运行中的机器人性能退化进行量化与呈现。初步实验结果表明，操作者在使用健康条期间，为降低高性能退化时机器人故障的风险，更倾向于采用手动控制模式。该研究还为后续人机协作实验提供了见解与经验启示。