Human-friendly interactive features are preferred for service robots used in emerging areas of robotic applications such as caretaking, health care, assistance, education and entertainment since they are intended to be operated by non-expert users. Humans prefer to use voice instructions, responses, and suggestions in their daily interactions. Such voice instructions and responses often include uncertain spatial descriptors such as “little” and “far”, which have no definitive quantitative meaning. Service robots involve direct interactions with human users through voice communication. Therefore, the ability to effectively quantify the meaning of such uncertain spatial descriptors is necessary for human-friendly service robots. This paper proposes a novel method to quantify the uncertain spatial descriptors in navigational instructions based on the current environmental setting and the influential notions conveyed by the pointing gestures that accompany voice instructions. The uncertain spatial descriptors are quantified by a fuzzy inference system that evaluates the spatial parameters of the current environment and the influential notions conveyed by pointing gestures, if available. According to the obtained experimental results, the proposed method is capable of improving the quantification ability of uncertain spatial descriptors by robots.

人性化的交互功能是新兴的机器人应用领域中首选的服务机器人，例如护理，保健，协助，教育和娱乐，因为它们旨在由非专业用户操作。人们更喜欢在日常互动中使用语音指令，回应和建议。这样的语音指令和响应通常包括不确定的空间描述符，例如“小”和“远”，没有明确的定量含义。服务机器人涉及通过语音通信与人类用户直接互动。因此，对于人类友好型服务机器人而言，有效量化此类不确定空间描述符的含义的能力是必需的。本文提出了一种新方法，可以基于当前环境设置和伴随语音指令的指向手势传达的有影响力的概念来量化导航指令中的不确定空间描述符。不确定的空间描述符由模糊推理系统量化，该系统评估当前环境的空间参数以及指向手势传达的有影响力的概念（如果有）。根据获得的实验结果，该方法能够提高机器人对不确定空间描述符的量化能力。不确定的空间描述符由模糊推理系统量化，该系统评估当前环境的空间参数以及指向手势传达的有影响力的概念（如果有）。根据获得的实验结果，该方法能够提高机器人对不确定空间描述符的量化能力。不确定的空间描述符由模糊推理系统量化，该系统评估当前环境的空间参数以及指向手势传达的有影响力的概念（如果有）。根据获得的实验结果，该方法能够提高机器人对不确定空间描述符的量化能力。