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Related Experiment Video

Updated: May 27, 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

Information-driven self-organization: the dynamical system approach to autonomous robot behavior.

Nihat Ay¹, Holger Bernigau, Ralf Der

¹Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. nay@mis.mpg.de

Theory in Biosciences = Theorie in Den Biowissenschaften

|November 30, 2011

Summary

Researchers are using predictive information (PI) to develop self-aware robots. Maximizing PI helps robots learn and adapt, enabling open-ended development and complex behaviors through local, Hebbian-like learning rules.

Related Experiment Videos

Last Updated: May 27, 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 and Artificial Intelligence
Information Theory
Computational Neuroscience

Background:

Living beings are viewed as information processing systems, with optimization conferring evolutionary advantages.
Robots increasingly require internal drives for innovation and curiosity to foster self-determined development.
Effective information measures are crucial for advancing these robotic capabilities.

Purpose of the Study:

To apply predictive information (PI), also known as excess entropy, to the dynamical systems approach in robot control.
To investigate the use of PI as a measure for information in sensorimotor processes for robots.
To derive learning rules for robot controllers based on maximizing PI.

Main Methods:

Studied linear systems to derive exact results for PI and explicit learning rules.
Developed Hebbian-like, local learning rules where synaptic updates depend on directly available activities.
Applied the maximum PI principle to a two-dimensional system mimicking embodied locomotion.

Main Results:

Derived explicit, local, Hebbian-type learning rules for robot controller parameters.
Demonstrated that maximizing PI in a simulated robotic system leads to the recognition and amplification of latent behavioral modes.
Showcased the versatility of PI-based learning rules for self-organization in complex robotic systems.

Conclusions:

Predictive information provides a powerful framework for developing self-organizing robotic behaviors.
The derived learning rules enable robots to autonomously discover and enhance their inherent capabilities.
This approach offers a pathway towards more adaptive and innovative robotic systems.