Modern developments in speech-enabled drones and aerial manipulation systems (AMS) enable drones to have social interactions with people, which is important for therapeutic applications involving flight and above-eye-level monitoring in people’s homes, but not everyone will accept drones into their daily lives. Consistently assessing who would accept a socially assistive drone into their home is a challenge for roboticists. An animation-based Mechanical Turk survey (N = 176) found that acceptance of a voice-enabled AMS for fatigue – i.e., physical or mental tiredness in the participant’s life – was higher among younger adults with higher education and longer symptoms of fatigue, suggesting demographics and a need for the task performed by the drone are critical factors for drone acceptance. Participants rated the drone as more acceptable for others than for themselves, demonstrating a self-other effect. A second video-based YouGov survey (N = 404) found that younger adults rated an AMS for managing the symptom of day-to-day fatigue as more acceptable than older adults. The self-other effect was reduced among participants who read a situation with specific versus general phrasing of the AMS’s imagined use, suggesting that it may be caused by an attribution bias. These results demonstrate how analyzing demographics and specifying the wording of technology use can more consistently assess to whom drones for fatigue are acceptable, which is of interest to public opinion researchers and roboticists.

语音无人机和空中操纵系统 (AMS) 的现代发展使无人机能够与人进行社交互动，这对于涉及飞行和人们家中高度视线监控的治疗应用非常重要，但并不是每个人都会接受无人机进入自己的家中。日常生活。对于机器人专家来说，持续评估谁会接受社交辅助无人机进入家中是一个挑战。一项基于动画的 Mechanical Turk 调查 ( N  = 176) 发现，在受过高等教育且疲劳症状较长的年轻人中，接受语音 AMS 治疗疲劳（即参与者生活中的身体或精神疲劳）的接受度较高，这表明人口统计数据和对无人机执行任务的需求是无人机接受度的关键因素。参与者认为无人机对其他人来说比自己更容易接受，表现出自我他人效应。第二项基于视频的 YouGov 调查（N  = 404）发现，年轻人认为 AMS 管理日常疲劳症状比老年人更容易接受。在阅读了 AMS 想象使用的特定与一般措辞的情况的参与者中，自我他人效应降低了，这表明这可能是由归因偏差引起的。这些结果表明，分析人口统计数据和指定技术使用的措辞如何能够更一致地评估哪些人可以接受无人机疲劳驾驶，这引起了舆论研究人员和机器人专家的兴趣。