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Multi-scopic neuro-cognitive adaptation for legged locomotion robots.

Azhar Aulia Saputra1, Kazuyoshi Wada2, Shiro Masuda2

  • 1Graduate School of Systems Design, Tokyo Metropolitan University, Hino, Tokyo, 191-0065, Japan. azhar.aulia.s@gmail.com.

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Summary

This study introduces a novel neuro-cognitive model for multi-legged robots, integrating sensing, perception, and cognition for adaptable and optimal dynamic locomotion. The model achieves efficient, multi-scale adaptation for improved robot movement.

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Area of Science:

  • Robotics
  • Neuro-cognitive modeling
  • Ecological psychology

Background:

  • Dynamic locomotion in robots requires balancing adaptability and optimality.
  • Existing models often struggle to integrate multi-modal sensing, perception, and cognition seamlessly.
  • A multi-scopic approach (micro, meso, macro) is proposed to address these integration challenges.

Purpose of the Study:

  • To propose a novel neuro-cognitive model for multi-legged robot locomotion.
  • To integrate multi-modal sensing, ecological perception, and cognition using a multi-scopic framework.
  • To achieve both adaptability and optimality in robot locomotion across different timescales.

Main Methods:

  • Development of a neuro-cognitive model with distinct micro (sensing), meso (integration), and macro (cognition) levels.
  • Implementation of an attention mechanism for short-term locomotion control (macroscopic level).
  • Integration of bottom-up sensory data and top-down map information for localization and intention generation (mesoscopic level).

Main Results:

  • The proposed multi-scale neuro-cognitive model successfully demonstrated adaptability and optimality in multi-legged locomotion.
  • Efficient computational usage was achieved across short- to long-term adaptation scales.
  • The model effectively integrated interoceptive and exteroceptive sensory information for enhanced robot control.

Conclusions:

  • The multi-scopic neuro-cognitive model provides a viable framework for achieving dynamic and adaptive locomotion in robots.
  • This approach offers efficient computational solutions for complex robotic movement.
  • Future applications include robotics, cognitive science, and ecological psychology research.