Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation and rapid environmental coverage. To address this issue, we propose an active inference method, referred to as spatial concept formation with information gain-based active exploration (SpCoAE) that combines sequential Bayesian inference using particle filters and information gain-based destination determination in a probabilistic generative model. This study interprets the robot's action as a selection of destinations to ask the user, `What kind of place is this?' in the context of active inference. This study provides insights into the technical aspects of the proposed method, including active perception and exploration by the robot, and how the method can enable mobile robots to learn spatial concepts through active exploration. Our experiment demonstrated the effectiveness of the SpCoAE in efficiently determining a destination for learning appropriate spatial concepts in home environments.

翻译：自主机器人需要通过探索环境并与用户交互来学习不同场所的类别。然而，利用用户的语言指令准备训练数据集既耗时又费力。此外，有效的探索对于恰当的概念形成和快速的环境覆盖至关重要。为解决这一问题，我们提出了一种主动推理方法，称为基于信息增益主动探索的空间概念形成（SpCoAE），该方法将使用粒子滤波的序贯贝叶斯推理与基于信息增益的目的地确定相结合于概率生成模型中。本研究将机器人的动作解释为在主动推理背景下选择目的地以询问用户“这是什么类型的地方？”。本研究深入探讨了所提出方法的技术层面，包括机器人的主动感知与探索，以及该方法如何使移动机器人通过主动探索学习空间概念。我们的实验证明了SpCoAE在家庭环境中高效确定目的地以学习适当空间概念的有效性。