Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multimodal place category acquired from the robot's spatial experience including vision, speech-language, and self-position. The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as ‘Go to the kitchen’, via probabilistic inference on a Bayesian generative model using spatial concepts. Specifically, path planning was formalized as the maximization of probabilistic distribution on the path-trajectory under speech instruction, based on a control-as-inference framework. Furthermore, we described the relationship between probabilistic inference based on the Bayesian generative model and control problem including reinforcement learning. We demonstrated path planning based on human instruction using acquired spatial concepts to verify the usefulness of the proposed approach in the simulator and in real environments. Experimentally, places instructed by the user's speech commands showed high probability values, and the trajectory toward the target place was correctly estimated. Our approach, based on probabilistic inference concerning decision-making, can lead to further improvement in robot autonomy. GRAPHICAL ABSTRACT

中文翻译：

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基于空间概念的基于贝叶斯生成模型的概率推理的人类语音指令导航

机器人不仅需要自主学习空间概念，还需要将这些知识用于家庭环境中的各种任务。空间概念代表从机器人的空间体验中获得的多模态位置类别，包括视觉、语音语言和自我位置。本研究的目的是通过使用空间概念对贝叶斯生成模型进行概率推理，使移动机器人能够使用人类语音指令执行导航任务，例如“去厨房”。具体而言，基于控制即推理框架，路径规划被形式化为语音指令下路径轨迹上概率分布的最大化。此外，我们描述了基于贝叶斯生成模型的概率推理与包括强化学习在内的控制问题之间的关系。我们展示了基于人类指令的路径规划，使用获得的空间概念来验证所提出的方法在模拟器和真实环境中的有效性。在实验中，用户语音指令指示的地点显示出高概率值，并且正确估计了朝向目标地点的轨迹。我们的方法基于有关决策的概率推理，可以进一步提高机器人的自主性。图形概要 在实验中，用户语音指令指示的地点显示出高概率值，并且正确估计了朝向目标地点的轨迹。我们的方法基于有关决策的概率推理，可以进一步提高机器人的自主性。图形概要 在实验中，用户语音指令指示的地点显示出高概率值，并且正确估计了朝向目标地点的轨迹。我们的方法基于有关决策的概率推理，可以进一步提高机器人的自主性。图形概要