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Related Experiment Video

Updated: May 10, 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 of complex robotic behaviors.

Georg Martius¹, Ralf Der, Nihat Ay

¹Max Planck Institute for Mathematics, Leipzig, Germany. martius@mis.mpg.de

|June 1, 2013

Summary

This study introduces time-local predictive information (TiPI) to drive autonomous systems. TiPI translates information principles into synaptic dynamics, enabling complex behaviors in robots and avoiding the curse of dimensionality.

Related Experiment Videos

Last Updated: May 10, 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
Information Theory
Dynamical Systems

Background:

Information theory offers domain-invariant principles for autonomous systems.
Predictive Information (PI) is explored as a behavioral driver for sensorimotor processes.
Existing methods struggle with nonlinear, nonstationary systems.

Purpose of the Study:

To introduce time-local predictive information (TiPI) for autonomous systems.
To translate information principles into synaptic dynamics.
To demonstrate TiPI's effectiveness in complex, high-dimensional robotic systems.

Main Methods:

Development of time-local predictive information (TiPI).
Derivation of exact results and update rules within a dynamical systems framework.
Application to high-dimensional robotic systems and complex physical systems.

Main Results:

TiPI enables exact results and explicit controller parameter updates.
Demonstrated spontaneous cooperativity in decentralized control systems.
Humanoid robots exhibited high behavioral variety influenced by physics and environment.

Conclusions:

TiPI effectively translates information principles to synaptic dynamics.
TiPI facilitates exploration of behavior space through low-dimensional modes.
This approach offers a promising solution to the curse of dimensionality in learning systems.