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Open and closed-loop control systems

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Updated: Jun 2, 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 learning for robot control: a survey.

Duy Nguyen-Tuong¹, Jan Peters

¹Max-Planck Institute for Biological Cybernetics, Tübingen, Germany. duy.nguyen-tuong@tuebingen.mpg.de

Cognitive Processing

|April 14, 2011

Summary

This summary is machine-generated.

Robots need to learn internal models for control, moving beyond human-engineered physics. This survey explores model learning architectures and methods for autonomous robots to generate their own kinematic and dynamic models from data.

Related Experiment Videos

Last Updated: Jun 2, 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
Machine Learning

Background:

Robotics heavily relies on kinematic and dynamic models for robot control and understanding external objects.
Intelligent mammals utilize internal models for action generation, inspiring cognitive robotics.
Traditional robotics uses manually created models based on human physics insights.

Purpose of the Study:

To survey progress in model learning for robot control, focusing on kinematic and dynamic levels.
To explore how autonomous robots can automatically generate models from data streams.
To provide a comprehensive overview of model-based learning control in robotics.

Main Methods:

Reviewing various model learning architectures applicable to robotics.
Analyzing challenges in robotics that influence the choice of learning methods.
Examining successful case studies of model learning in robot control scenarios.

Main Results:

Identification of key challenges and suitable learning methods for robotic model acquisition.
Discussion of different model learning architectures for robot control.
Demonstration of successful applications of model-based learning control.

Conclusions:

Future autonomous robots require automatic model generation from sensory data.
Real-time learning algorithms are crucial for adaptive robot control.
Model learning is essential for advancing robot autonomy and cognitive capabilities.