情報利得に基づく能動的探索による効率的な空間概念形成
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation
パーティクルフィルタと情報利得を組み合わせ、ロボットがユーザーに質問しながら効率的に空間概念を学習する能動的推論手法を提案した。
著者: Akira Taniguchi, Yoshiki Tabuchi, Tomochika Ishikawa, Lotfi El Hafi, Yoshinobu Hagiwara, Tadahiro Taniguchi
分類: cs.RO, cs.AI
原文アブストラクト
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.
