论文标题
自然语言互动以促进远程机器人的心理模型
Natural Language Interaction to Facilitate Mental Models of Remote Robots
论文作者
论文摘要
越来越复杂和自主的机器人正在现实环境中部署，并带来深远的后果。高风险场景，例如紧急响应或离岸能源平台和核检查，要求机器人操作员对机器人可以做什么和无能为力具有清晰的心理模型。但是，操作员通常不是机器人的原始设计师，因此，他们不一定具有如此清晰的心理模型，尤其是当他们是新手使用者时。缺乏心理模型的清晰度会减慢采用率，并可能对人机组合产生负面影响。我们建议与对话助手充当调解人的互动，可以帮助用户了解远程机器人的功能，并通过自然语言解释提高透明度，并促进对操作员的心理模型的评估。
Increasingly complex and autonomous robots are being deployed in real-world environments with far-reaching consequences. High-stakes scenarios, such as emergency response or offshore energy platform and nuclear inspections, require robot operators to have clear mental models of what the robots can and can't do. However, operators are often not the original designers of the robots and thus, they do not necessarily have such clear mental models, especially if they are novice users. This lack of mental model clarity can slow adoption and can negatively impact human-machine teaming. We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well as facilitate the evaluation of operators' mental models.
