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Learning to crawl: Benefits and limits of centralized versus distributed control
Luca Gagliardi1, Agnese Seminara1
1University of Genoa, Machine Learning Genoa Center and Department of Civil Chemical and Environmental Engineering, villa Cambiaso, via Montallegro 1, 16145, Italy.
Physical Review. E
|May 16, 2026
Summary
This study models a learning crawler, finding that centralized control enhances speed and robustness but increases computation. Distributed control is cheaper but slower, with hierarchical organization balancing these trade-offs.
Area of Science:
- Robotics
- Computational Neuroscience
- Biomimicry
Background:
- Crawling locomotion is complex, involving sensory feedback and motor control.
- Understanding how biological and artificial systems learn efficient movement is crucial.
Purpose of the Study:
- To model a learning crawler with distributed suction units.
- To investigate the impact of centralized versus distributed learning architectures on crawling performance.
- To explore trade-offs between speed, robustness, and computational cost.
Main Methods:
- A computational model of a linear crawler with spring-connected suction units.
- Implementation of endogenous muscular contraction waves.
- Application of tabular Q-learning for adhesion pattern learning.
- Analysis of centralized, distributed, and hierarchical control architectures.
Main Results:
- Crawling can be learned through trial and error using Q-learning.
- Centralized control improves speed and robustness by leveraging long-range correlations.
- Distributed control is computationally cheaper but results in slower, jerkier movement.
- Hierarchical control offers a balance between performance and computational cost.
Conclusions:
- Centralization in learning architectures enhances crawling efficiency and resilience.
- Distributed systems offer computational savings at the cost of performance.
- Hierarchical organization provides an optimal balance for crawling systems.
- Findings inform the design of bio-inspired robotic crawlers and understanding biological locomotion.
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