# Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances

arXiv: https://arxiv.org/abs/2106.08574 · HTML: https://mlsanity.com/paper/2106.08574/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions](https://mlsanity.com/paper/1710.06280/) (2017)
- [Symbol Emergence in Robotics: A Survey](https://mlsanity.com/paper/1509.08973/) (2015)
- [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)
- [Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model](https://mlsanity.com/paper/2002.07381/) (2020)
- [Robot Language Learning, Generation, and Comprehension](https://mlsanity.com/paper/1508.06161/) (2015)
- [Grounded Lexicon Acquisition - Case Studies in Spatial Language](https://mlsanity.com/paper/1607.07630/) (2016)

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

- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues](https://mlsanity.com/paper/2201.06786/) (2022)

## Abstract (arXiv)

This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be able to acquire linguistic representation and its meaning specific to an environment through interactions with humans as children do. Specifically, relative spatial concepts (e.g., front and right) are widely used in our daily lives, however, it is not obvious which object is a reference object when a robot learns relative spatial concepts. Therefore, we propose methods by which a robot without prior knowledge of words can learn relative spatial concepts. The methods are formulated using a probabilistic model to estimate the proper reference objects and distributions representing concepts simultaneously. The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object. Additionally, we show that two processes in the proposed method improve the estimation accuracy of the concepts: generating candidate word sequences by class n-gram and selecting word sequences using location information. Furthermore, we show that clues to reference objects improve accuracy even though the number of candidate reference objects increases.