Lexical Acquisition from Audio-Visual Streams Using a Multimodal Recurrent State-Space Model

Komura, Soichiro; Maeyama, Katsuyoshi; Taniguchi, Akira; Taniguchi, Tadahiro

doi:10.1109/icdl55364.2023.10364429

2023 IEEE International Conference on Development and Learning (ICDL) 2023

DOI: 10.1109/icdl55364.2023.10364429

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Lexical Acquisition from Audio-Visual Streams Using a Multimodal Recurrent State-Space Model

Soichiro Komura,

Katsuyoshi Maeyama,

Akira Taniguchi

et al.

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2024

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Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

Nakashima,

Otake,

Taniguchi

et al. 2024

Front. Comput. Neurosci.

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It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state—space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.

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Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

Nakashima,

Otake,

Taniguchi

et al. 2024

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Lexical Acquisition from Audio-Visual Streams Using a Multimodal Recurrent State-Space Model

Cited by 1 publication

References 14 publications

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

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