Department of Psychology, University of Notre Dame.
Top Cogn Sci. 2014 Jul;6(3):513-33. doi: 10.1111/tops.12101. Epub 2014 Jun 20.
This contribution presents a corpus of spatial descriptions and describes the development of a human-driven spatial language robot system for their comprehension. The domain of application is an eldercare setting in which an assistive robot is asked to "fetch" an object for an elderly resident based on a natural language spatial description given by the resident. In Part One, we describe a corpus of naturally occurring descriptions elicited from a group of older adults within a virtual 3D home that simulates the eldercare setting. We contrast descriptions elicited when participants offered descriptions to a human versus robot avatar, and under instructions to tell the addressee how to find the target versus where the target is. We summarize the key features of the spatial descriptions, including their dynamic versus static nature and the perspective adopted by the speaker. In Part Two, we discuss critical cognitive and perceptual processing capabilities necessary for the robot to establish a common ground with the human user and perform the "fetch" task. Based on the collected corpus, we focus here on resolving the perspective ambiguity and recognizing furniture items used as landmarks in the descriptions. Taken together, the work presented here offers the key building blocks of a robust system that takes as input natural spatial language descriptions and produces commands that drive the robot to successfully fetch objects within our eldercare scenario.
本研究贡献了一个空间描述语料库，并描述了人类驱动的空间语言机器人系统的开发，用于理解这些描述。应用领域是在老年人护理环境中，根据居民提供的自然语言空间描述，辅助机器人为老年人“获取”物体。在第一部分中，我们描述了从一组老年人在模拟老年人护理环境的虚拟 3D 家中自然产生的描述语料库。我们对比了参与者对人类和机器人化身进行描述的情况，以及在告知收件人如何找到目标和目标位置的指令下进行描述的情况。我们总结了空间描述的关键特征，包括它们的动态和静态性质以及说话者采用的视角。在第二部分中，我们讨论了机器人与人类用户建立共同基础并执行“获取”任务所需的关键认知和感知处理能力。基于收集到的语料库，我们在这里重点解决视角歧义问题，并识别描述中用作地标家具物品。总之，这里呈现的工作提供了一个强大系统的关键构建块，该系统接收自然空间语言描述作为输入，并生成命令，驱动机器人在我们的老年人护理场景中成功获取物体。
