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: 1111494
• 负责人: Benjamin Kuipers
• 金额: $ 69.33万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111494&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 预计未来人类和智能机器人将协作完成共同任务。 为了实现这一愿景，机器人必须拥有足够丰富的任务领域知识，并且这些知识必须能够以支持人类和机器人之间有效沟通的方式使用。 导航空间是为数不多的任务领域之一，其中的知识结构足以使物理体现的机器人代理成为有用的协作者，满足人类的真正需求。 在该项目中，PI 将开发和评估一种智能机器人，该机器人能够真正对人类有用，并且能够与人类就其共同任务进行自然对话。混合空间语义层次结构 (HSSH) 是一种受人类启发的导航空间知识的多本体表示。 HSSH 中的空间表示提供了高效的增量学习、资源限制下的优雅降级以及不同类型的人机交互的自然界面。 语音是使用自然语言与机器人进行交流的一种自然但要求较高的方式。 为了保持实时性能，必须组织自然语言理解，以最大限度地减少根据后来的信息从早期结论中回溯的数量。 该项目将回答三个科学问题。（1）通过实时计算机视觉扩展的HSSH框架能否表达与导航任务相关的自然人类环境知识？ （2）HSSH表示能否支持空间导航领域有效的自然语言通信？ 3) 我们能否开发出有效的人机交互来满足人的需求并提高系统的性能？为此，PI 将使用两种不同类型的导航机器人进行这项研究，每种机器人都从其旅行经验中学习并构建日益复杂的认知地图：一种是智能机器人轮椅，可将人类驾驶员带到所需的目的地；另一种是远程呈现机器人，可将其感知传输给远程人类驾驶员。 在环境中导航，以便驾驶员能够实现虚拟存在并与其他人远程通信。 为了为设计流程提供信息，PI 将与潜在用户进行焦点小组讨论。 他们还将在整个过程中评估其实施的系统，创建一个迭代的设计-测试周期。 更广泛的影响：为了取得成功，智能机器人不仅必须能够感知世界，表达它所学到的知识，做出有用的推论和计划，并有效地行动。 它还必须能够与其他代理，特别是与人进行有效的沟通。 扎根的知识表示、情境自然语言理解和人机交互之间的融合在智力上是基础性的，也是本研究的重点。 由于空间知识领域几乎是人类知识所有方面的基础，因此项目成果将具有广泛的适用性。 这项工作将为感知（失明或低视力）、认知（发育迟缓或痴呆）或一般虚弱（老年）残疾人士创造移动辅助技术。 它还将支持远程呈现应用，例如远程办公、远程医疗和搜索救援。 该项目包括在多个场所向 K-12 和社区学院学生、K-12 教师和公众进行推广。