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.

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A growing elderly population has created a need for innovative eldercare technologies. The use of a home robot to assist with daily tasks is one such example. In this paper we describe an interface for human-robot interaction, which uses built-in speech recognition in Android phones to control a mobile robot. We discuss benefits of using a smartphone for speech-based robot control and present speech recognition accuracy results for younger and older adults obtained with an Android smartphone.

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This paper proposes a system that allows the use of natural spatial language to control a robot performing a fetch task in an indoor environment. The system processes spatial referencing language and extracts a tree structure of language chunks. The spatial language system is then grounded to a robot navigation instruction in the form of a sequence of actions based on spatial references to furniture and room structure; the best navigation instruction is selected by scoring. In addition, the Reference-Direction-Target (RDT) model is proposed to represent indoor robot actions. To control the robot for the fetch task, a behavior model is designed based on the RDT model. An assistive robot has been designed and programmed based on this system. The proposed spatial language grounding model and robot behavior model are tested experimentally in three sets of experiments. Results show that the system enables a robot to follow spatial language commands in a physical indoor environment even if the referenced furniture items are re-positioned.978-1-4799-6934-0/14/$31.00 ©2014 IEEE