# Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model

arXiv: https://arxiv.org/abs/2002.07381 · HTML: https://mlsanity.com/paper/2002.07381/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)
- [Map completion from partial observation using the global structure of multiple environmental maps](https://mlsanity.com/paper/2103.09071/) (2021)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances](https://mlsanity.com/paper/2106.08574/) (2021)
- [Toward an Idiomatic Framework for Cognitive Robotics](https://mlsanity.com/paper/2111.13027/) (2021)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information](https://mlsanity.com/paper/2103.06442/) (2021)
- [Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping](https://mlsanity.com/paper/2203.10820/) (2022)
- [Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality](https://mlsanity.com/paper/2306.15837/) (2023)

## Abstract (arXiv)

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.