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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1398851
This article is part of the Research Topic Brain-Inspired Intelligence: the Deep Integration of Brain Science and Artificial Intelligence View all 4 articles

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

Provisionally accepted
Takeshi Nakashima Takeshi Nakashima 1*Shunsuke Otake Shunsuke Otake 2Akira Taniguchi Akira Taniguchi 3Katsuyoshi Maeyama Katsuyoshi Maeyama 1Lotfi El Hafi Lotfi El Hafi 4Tadahiro Taniguchi Tadahiro Taniguchi 3Hiroshi Yamakawa Hiroshi Yamakawa 5,6,7
  • 1 Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
  • 2 Osaka University, Suita, Ōsaka, Japan
  • 3 College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
  • 4 Research Organization of Science and Technology, Ritsumeikan University, Shiga, Japan
  • 5 Whole Brain Architecture Initiative (WBAI), Edogawa-ku, Tokyo, Japan
  • 6 The University of Tokyo, Bunkyo, Tōkyō, Japan
  • 7 RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan

The final, formatted version of the article will be published soon.

    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/.

    Keywords: allocentric, brain-inspired AI, Egocentric, Kidnapped robot problem, Monte Carlo localization, Probabilistic Generative Model, world model

    Received: 10 Mar 2024; Accepted: 29 May 2024.

    Copyright: © 2024 Nakashima, Otake, Taniguchi, Maeyama, El Hafi, Taniguchi and Yamakawa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Takeshi Nakashima, Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga, 525-0058, Japan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.