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Related Experiment Video

Updated: Nov 6, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Continuous learning of emergent behavior in robotic matter.

Giorgio Oliveri¹, Lucas C van Laake¹, Cesare Carissimo¹

¹Designer Matter Department, AMOLF, 1098 XG Amsterdam, The Netherlands.

Proceedings of the National Academy of Sciences of the United States of America

|May 11, 2021

Summary

Robots can learn and adapt using simple, decentralized units. This modular approach enables robust, scalable robotic systems without complex controllers or communication.

Keywords:

dynamic environment emergent behavior modular robot reinforced learning

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Area of Science:

Robotics
Artificial Intelligence
Materials Science

Background:

Developing adaptive and autonomous robots is a key challenge.
Current methods often rely on complex centralized controllers or machine learning.
A need exists for simpler, scalable learning strategies in robotics.

Purpose of the Study:

To investigate a decentralized and modular approach for robot learning.
To identify requirements for robust and scalable learning in robotic systems.
To explore the potential of "robotic matter" for autonomous adaptation.

Main Methods:

Experiments and simulations on a robotic platform of identical autonomous units.
Decentralized control where each unit adapts independently using a Monte Carlo scheme.
Utilizing physical connections between units for learning, without external communication.

Main Results:

The assembled system learned and maintained optimal behavior in dynamic environments.
The system demonstrated robustness to damage, provided memory remained representative.
Physical connections alone were sufficient for distributed learning.

Conclusions:

Decentralized, modular control enables scalable and robust robot learning.
This approach blurs the line between materials and robots, creating "robotic matter".
Such systems can autonomously adapt to dynamic or unfamiliar environments, with applications in medicine and space exploration.