HCC: Large: Collaborative Research: Human-Robot Dialog for Collaborative Navigation Tasks

HCC：大型：协作研究：用于协作导航任务的人机对话

• 批准号: 1111323
• 负责人: Matthias Scheutz
• 金额: $ 38.53万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111323&HistoricalAwards=false
• 关键词: HCC; Large; Collaborative; Research; Human

This research involves collaboration among investigators at three institutions. The PIs anticipate a future in which humans and intelligent robots will collaborate on shared tasks. To achieve this vision, a robot must have sufficiently rich knowledge of the task domain and that knowledge must be usable in ways that support effective communication between a human and the robot. Navigational space is one of the few task domains where the structure of the knowledge is sufficiently well understood for a physically-embodied robot agent to be a useful collaborator, meeting genuine human needs. In this project, the PIs will develop and evaluate an intelligent robot capable of being genuinely useful to a human, and capable of natural dialog with a human about their shared task.The Hybrid Spatial Semantic Hierarchy (HSSH) is a human-inspired multi-ontology representation for knowledge of navigational space. The spatial representations in the HSSH provide for efficient incremental learning, graceful degradation under resource limitations, and natural interfaces for different kinds of human-robot interactions. Speech is a natural though demanding way to use natural language to communicate with a robot. To maintain real-time performance, natural language understanding must be organized to minimize the amount of backtracking from early conclusions in light of later information. This project will answer three scientific questions.(1) Can the HSSH framework, extended with real-time computer vision, express the kinds of knowledge of natural human environments that are relevant to navigation tasks? (2) Can the HSSH representation support effective natural language communication in the spatial navigation domain? 3) Can we develop effective human-robot interaction that meets the needs of a person and improves the performance of the system?To these ends, the PIs will perform this research with two different kinds of navigational robots, each learning from its travel experiences and building an increasingly sophisticated cognitive map: an intelligent robotic wheelchair which carries its human driver to desired destinations, and a telepresence robot that transmits its perceptions to a remote human driver as it navigates within an environment so the driver can achieve virtual presence and communicate with others remotely. To inform the design process, the PIs will conduct focus groups with potential users. They will also evaluate their implemented systems throughout the process, creating an iterative design-test cycle.Broader Impacts: To be successful, an intelligent robot must not only be able to perceive the world, represent what it learns, make useful inferences and plans, and act effectively. It must also be able to communicate effectively with other agents, and particularly with people. This confluence among grounded knowledge representation, situated natural language understanding, and human-robot interaction is intellectually fundamental, and is the focus of this research. Since the domain of spatial knowledge is foundational for virtually all aspects of human knowledge, project outcomes will have broad applicability. This work will create technologies for mobility assistance for people with disabilities in perception (blindness or low vision), cognition (developmental delay or dementia), or general frailty (old age). It will also support telepresence applications such as telecommuting, telemedicine and search and rescue. The project includes outreach to K-12 and community college students, K-12 teachers, and the public in a number of venues.

这项研究涉及三个机构的研究人员之间的合作。 PI可以预料到人类和智能机器人将在共同任务上进行合作的未来。 为了实现这一愿景，机器人必须对任务领域具有足够丰富的知识，并且知识必须以支持人与机器人之间有效沟通的方式可用。 导航空间是为数不多的任务域之一，在该任务领域中，知识的结构充分理解，使身体体现的机器人代理人成为有用的合作者，可以满足真正的人类需求。 在这个项目中，PI将开发和评估能够对人类真正有用的智能机器人，并且能够与人类有关其共同任务的自然对话。混合空间空间语义层次结构（HSSH）是一种人类启发的人类启发的多种主体学代表，以了解航海空间的知识。 HSSH中的空间表示提供了有效的增量学习，在资源限制下优美的降解以及用于不同类型的人类机器人相互作用的自然界面。 言语是一种自然而然的使用自然语言与机器人沟通的自然方法。 为了保持实时表现，必须组织自然语言理解，以根据以后的信息来最大程度地减少早期结论的回溯量。 该项目将回答三个科学问题。（1）使用实时计算机视觉扩展的HSSH框架可以表达与导航任务相关的自然人类环境知识的种类吗？ （2）HSSH表示能否支持空间导航域中有效的自然语言交流？ 3）我们能否开发有效的人像互动来满足人的需求并提高系统的性能？对这些目的，PI将使用两种不同类型的导航机器人进行这项研究，每个机器人都可以从旅行体验中学习，并建立一个越来越复杂的认知型：智能的机器人椅子，它带来了人类驾驶员的远程驾驶员，并携带了远距离的人，并触及了人类的远程访问，并触及了远程的触觉，并且是远程触觉的远程启动，并具有远距离的远程效果。在环境中导航，以便驾驶员可以实现虚拟的存在并远程与他人沟通。 为了告知设计过程，PI将与潜在用户进行焦点小组。 他们还将在整个过程中评估他们的实施系统，创建一个迭代设计测试周期。Broader的影响：要成功，智能机器人不仅必须能够感知世界，代表世界所学，做出有用的推论和计划，并有效地行事。 它还必须能够与其他代理，尤其是与人进行有效的沟通。 扎根的知识表示，自然语言理解和人类机器人互动之间的这种融合在智力上是基本的，并且是这项研究的重点。 由于空间知识的领域几乎是人类知识的所有方面的基础，因此项目成果将具有广泛的适用性。 这项工作将为在感知（失明或低视力），认知（发育延迟或痴呆症）或一般脆弱（老年）中为残疾人提供流动性援助的技术。 它还将支持远程诉讼的应用程序，例如远程办公，远程医疗和搜索和救援。 该项目包括向K-12和社区大学生，K-12老师和公众的宣传。