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Related Experiment Video

Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Model-based learning for mobile robot navigation from the dynamical systems perspective.

¹Sony Comput. Sci. Lab. Inc., Tokyo.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1996

Summary

This study shows how robots can develop symbolic thinking by learning environmental models through navigation. This approach grounds internal processes in physical interactions, enabling complex action planning.

Related Experiment Videos

Last Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Artificial Intelligence
Cognitive Science

Background:

Behavior-based robots traditionally struggle with deliberative thinking and symbolic representation.
The symbol grounding problem and situatedness of internal processes are key challenges in AI.

Purpose of the Study:

To investigate how behavior-based robots can form symbolic processes for deliberative thinking.
To address the symbol grounding problem and situatedness of internal processes in robots.
To explore the application of dynamical systems to robot navigation learning.

Main Methods:

Utilizing a forward modeling scheme with recurrent neural learning for robot navigation.
Applying a dynamical systems approach to model environmental interactions.
Employing a mobile robot with a laser range finder for experimental validation.

Main Results:

The robot learned grammatical structure from local sensory inputs and navigational experience.
The robot generated diverse action plans for goal-reaching using a learned forward model with chaotic dynamics.
Internal symbolic processes were shown to be grounded and situated through self-organization and interaction with the physical world.

Conclusions:

A dynamical systems approach enables behavior-based robots to develop grounded, situated symbolic processes.
Robot learning of environmental geometry and action planning is achieved through recurrent neural networks and forward models.
The interaction between neural and environmental dynamics ensures structural stability and situatedness of symbolic processes.