你的指示可能很明确，但我不清楚！
Your instruction may be crisp, but not clear to me!
摘要：
部署在我们日常生活中的机器人数量正在不断增加。即使在工业设置中，同事机器人的使用也在迅速增加。这些同居机器人执行各种任务的指示，由共同定位的人类。因此，自然的交互机制在机器人的可用性和可接受性中起着很大的作用，特别是对于非专家用户。自然语言处理（NLP）的最新发展为聊天机器人为用户的查询生成自动响应铺平了道路。机器人可以配备有这样的对话系统。然而，人机交互的目标并不集中在生成对查询的响应上，而是经常涉及在物理世界中执行一些任务。因此，需要一种系统，该系统可以从自然指令以及前置条件和后置条件的集合中检测用户意图的任务。在这项工作中，我们开发了一个对话引擎的机器人，可以分类和映射任务指令的机器人的能力。如果在指令中有一些歧义或缺少一些所需的信息，这在自然对话中经常发生，它会提出一个适当的问题来解决它。目标是为用户生成最小的和精确的查询来解决歧义。我们评估我们的系统的远程呈现的情况下，远程用户指示机器人的各种任务。我们的研究基于12个人表明，建议的对话策略可以帮助新手用户有效地与机器人进行交互，从而获得满意的用户体验。
英文摘要：
The number of robots deployed in our daily surroundings is ever-increasing. Even in the industrial set-up, the use of coworker robots is increasing rapidly. These cohabitant robots perform various tasks as instructed by co-located human beings. Thus, a natural interaction mechanism plays a big role in the usability and acceptability of the robot, especially by a non-expert user. The recent development in natural language processing (NLP) has paved the way for chatbots to generate an automatic response for users' query. A robot can be equipped with such a dialogue system. However, the goal of human-robot interaction is not focused on generating a response to queries, but it often involves performing some tasks in the physical world. Thus, a system is required that can detect user intended task from the natural instruction along with the set of pre- and post-conditions. In this work, we develop a dialogue engine for a robot that can classify and map a task instruction to the robot's capability. If there is some ambiguity in the instructions or some required information is missing, which is often the case in natural conversation, it asks an appropriate question(s) to resolve it. The goal is to generate minimal and pin-pointed queries for the user to resolve an ambiguity. We evaluate our system for a telepresence scenario where a remote user instructs the robot for various tasks. Our study based on 12 individuals shows that the proposed dialogue strategy can help a novice user to effectively interact with a robot, leading to satisfactory user experience.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
