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

arXiv: https://arxiv.org/abs/2211.10934 · HTML: https://mlsanity.com/paper/2211.10934/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 91 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 30 of 35)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation](https://mlsanity.com/paper/1811.10092/) (2018)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [Topological Planning with Transformers for Vision-and-Language Navigation](https://mlsanity.com/paper/2012.05292/) (2020)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [An Exploration of Embodied Visual Exploration](https://mlsanity.com/paper/2001.02192/) (2020)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Learning to Map for Active Semantic Goal Navigation](https://mlsanity.com/paper/2106.15648/) (2021)
- [SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency](https://mlsanity.com/paper/2112.01001/) (2021)
- [Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks](https://mlsanity.com/paper/1301.3853/) (2013)
- [Semantic Curiosity for Active Visual Learning](https://mlsanity.com/paper/2006.09367/) (2020)
- [Symbol Emergence in Cognitive Developmental Systems: a Survey](https://mlsanity.com/paper/1801.08829/) (2018)
- [Ready Policy One: World Building Through Active Learning](https://mlsanity.com/paper/2002.02693/) (2020)
- [PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference](https://mlsanity.com/paper/2003.00370/) (2020)
- [Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration](https://mlsanity.com/paper/2112.04063/) (2021)
- [Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping](https://mlsanity.com/paper/1803.03481/) (2018)
- [Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping](https://mlsanity.com/paper/1704.04664/) (2017)
- [Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions](https://mlsanity.com/paper/1903.09243/) (2019)
- [Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model](https://mlsanity.com/paper/2002.07381/) (2020)
- [Information-based Active SLAM via Topological Feature Graphs](https://mlsanity.com/paper/1509.08155/) (2015)
- [Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robots](https://mlsanity.com/paper/2002.03671/) (2020)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)

## Built on by (most cited first, 2 of 2)

- [Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model](https://mlsanity.com/paper/2509.12754/) (2025)
- [Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration](https://mlsanity.com/paper/2608.14466/) (2026)

## Abstract (arXiv)

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.