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Updated: Jul 24, 2025
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
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On the Criticality of Adaptive Boolean Network Robots
Michele Braccini1, Andrea Roli1,2, Edoardo Barbieri1
1Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy.
Entropy (Basel, Switzerland)
|July 8, 2023
Summary
Robots controlled by critical Boolean networks show enhanced performance and adaptation capabilities. Dynamical criticality in robot control systems offers advantages for online adaptation and improved metrics.
Area of Science:
- Robotics
- Complex Systems
- Artificial Intelligence
Background:
- Dynamical criticality in systems balances robustness and responsiveness.
- Boolean networks are used in artificial classifiers and robot control.
- Online adaptation allows robots to improve performance over time.
Purpose of the Study:
- Investigate the role of dynamical criticality in online robot adaptation.
- Examine how adapting Boolean networks affects robot performance.
- Compare adaptation via coupling changes versus structural changes.
Main Methods:
- Robots controlled by random Boolean networks (RBNs) were studied.
- RBNs were adapted in their sensor/actuator couplings, structure, or both.
- Performance metrics were evaluated for robots under different adaptation strategies.
- Dynamical regimes (ordered, critical, disordered) were analyzed.
Main Results:
- Critical RBN-controlled robots achieved higher average and maximum performance.
- Adaptation via coupling changes generally yielded slightly better performance than structural changes.
- Ordered networks adapted structurally tended to reach the critical regime.
- Criticality enhances robot adaptation and performance.
Conclusions:
- Dynamical criticality is advantageous for robot online adaptation.
- Calibrating robot control systems at critical states is beneficial.
- Critical regimes facilitate adaptation in complex systems.
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