Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation

TL;DR

This paper introduces SpCoAE, an active exploration method for robots that uses information gain and particle filters to efficiently learn spatial concepts through interaction and exploration in home environments.

Contribution

The study presents a novel active inference approach combining particle filters and information gain for efficient spatial concept formation in robots.

Findings

Effective destination selection for learning spatial concepts

Improved efficiency in environmental exploration

Successful application in home environments

Abstract

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.