Online spatial concept and lexical acquisition with simultaneous localization and mapping

Taniguchi, Akira; Hagiwara, Yoshinobu; Taniguchi, Tadahiro; Inamura, Tetsunari

doi:10.1109/iros.2017.8202243

Cited by 50 publications

(82 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Creating a robot that can learn language from its own sensorimotor experience alone is one of our challenges, which is an essential element for the understanding of symbol emergence in cognitive systems. Many studies have been exploring the challenge in modeling language acquisition in developmental process using neural networks [91], [124], [152] and probabilistic models [5], [88], [153], [154].…”

Section: Language Acquisition By a Robotmentioning

confidence: 99%

Symbol Emergence in Cognitive Developmental Systems: A Survey

Taniguchi

Piater

Wörgötter

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

Self Cite

View full text Add to dashboard Cite

Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems. Fig. 1. Robot in a home environment that has to deal with complex manipulation, planning, and interaction via semiotic communication with human users.

show abstract

Section: Language Acquisition By a Robotmentioning

confidence: 99%

Symbol Emergence in Cognitive Developmental Systems: A Survey

Taniguchi

Piater

Wörgötter

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 2 shows the graphical model of SpCoSLAM and lists each variable of the graphical model. The details of the formulation of the generation process represented by the graphical model are described in Taniguchi et al (2017). The method learns sequential spatial concepts for unknown environments without maps.…”

Section: Overviewmentioning

confidence: 99%

“…Therefore, we developed in previous work an online algorithm, SpCoSLAM , that can sequentially learn a map, a lexicon, and spatial concepts to integrate positions, speech signals, and scene images. In Taniguchi et al (2017), however, the accuracy was inferior to that of SpCoA. In this paper, we also compare our proposal to the latest batch learning method, SpCoA++.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of online learning, previous knowledge can be used immediately for reasoning and tasks such as language communication. Taniguchi et al (2017) focused on deriving and constructing an appropriate online learning algorithm mathematically based on a theory of machine learning. In our previous work, we proposed SpCoSLAM as an integrated model of nonparametric Bayesian multimodal categorization, a Bayesian filter-based SLAM, speech recognition, and word segmentation, from the standpoint of unsupervised machine learning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved and scalable online learning of spatial concepts and language models with mapping

et al. 2020

Self Cite

View full text Add to dashboard Cite

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

show abstract

“…This simple visual recognitionbased approach also has the same problems. Spatial concept formation methods have been developed to enable robots to acquire place-related words as well as estimate categories and regions (Ishibushi et al, 2015;Taniguchi et al, 2016bTaniguchi et al, , 2017Taniguchi et al, , 2018. These methods can estimate the number of categories using the Dirichlet process (Teh et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

et al. 2019

Self Cite

View full text Add to dashboard Cite

An autonomous robot performing tasks in a human environment needs to recognize semantic information about places. Semantic mapping is a task in which suitable semantic information is assigned to an environmental map so that a robot can communicate with people and appropriately perform tasks requested by its users. We propose a novel statistical semantic mapping method called SpCoMapping, which integrates probabilistic spatial concept acquisition based on multimodal sensor information and a Markov random field applied for learning the arbitrary shape of a place on a map.SpCoMapping can connect multiple words to a place in a semantic mapping process using user utterances without pre-setting the list of place names. We also develop a nonparametric Bayesian extension of SpCoMapping that can automatically estimate an adequate number of categories. In the experiment in the simulation environments, we showed that the proposed method generated better semantic maps than previous semantic mapping methods; our semantic maps have categories and shapes similar to the ground truth provided by the user. In addition, we showed that SpCoMapping could generate appropriate semantic maps in a real-world environment.

show abstract

Online spatial concept and lexical acquisition with simultaneous localization and mapping

Cited by 50 publications

References 21 publications

Symbol Emergence in Cognitive Developmental Systems: A Survey

Symbol Emergence in Cognitive Developmental Systems: A Survey

Improved and scalable online learning of spatial concepts and language models with mapping

Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

Contact Info

Product

Resources

About