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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

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Published on: November 24, 2015

The eMOSAIC model for humanoid robot control.

Norikazu Sugimoto1, Jun Morimoto, Sang-Ho Hyon

  • 1National Institute of Communication Telecommunication, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto 619-0288, Japan. xsugi@nict.go.jp

Neural Networks : the Official Journal of the International Neural Network Society
|February 28, 2012
PubMed
Summary
This summary is machine-generated.

This study extends the MOSAIC architecture with state estimators for adaptive control in real humanoid robots. The enhanced model successfully enabled complex behaviors like squatting and object carrying.

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Area of Science:

  • Robotics
  • Neuroscience
  • Control Theory

Background:

  • The MOSAIC architecture, originally for understanding human adaptive control, offers a modular approach for complex systems.
  • Humanoid robots share nonlinear dynamics and environmental interaction challenges with humans, necessitating robust control strategies.
  • Existing MOSAIC applications are limited to simulations due to sensitivity to noise and partial observability.

Purpose of the Study:

  • To extend the MOSAIC architecture for controlling real humanoid robots.
  • To address limitations of the original MOSAIC model in noisy and partially observable environments.
  • To enable the generation of complex motor behaviors in humanoid robots.

Main Methods:

  • Integration of state estimators into the MOSAIC architecture.
  • Development of an extended MOSAIC model capable of handling real-world sensory data.
  • Testing the extended model on a physical humanoid robot platform.

Main Results:

  • Successfully implemented state estimators to manage observation noise and partial observability.
  • Demonstrated the generation of squatting behaviors on a real humanoid robot.
  • Achieved successful object-carrying behaviors using the extended MOSAIC model.

Conclusions:

  • The extended MOSAIC architecture with state estimators is effective for real-world humanoid robot control.
  • This approach bridges the gap between neuroscience-inspired models and practical robotic applications.
  • The enhanced MOSAIC model shows significant potential for human motor control research and advanced robotics.