Emergent Heterogeneous Swarm Control Through Hebbian Learning

2025-07-14

AI Summary

To address key bottlenecks in swarm robotics—including reliance on prior knowledge for heterogeneous control, decoupling between microscopic agent behavior and macroscopic swarm dynamics, and the curse of dimensionality—this paper proposes a heterogeneity-enabled self-organization mechanism based on Hebbian-style local learning. Departing from global modeling and centralized design, the approach employs biologically inspired synaptic plasticity rules to achieve distributed, online behavioral differentiation among agents, with learning parameters optimized via evolutionary algorithms. Relying solely on local sensory inputs, the system spontaneously generates functional specialization and collective behavioral transitions, drastically reducing dependence on domain-specific prior knowledge. Evaluated on standard benchmark tasks, the framework demonstrates superior environmental adaptability and robustness compared to conventional methods. Results validate Hebbian learning as a viable alternative paradigm to multi-agent reinforcement learning, offering a novel pathway toward decentralized swarm intelligence.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we introduce Hebbian learning as a novel method for swarm robotics, enabling the automatic emergence of heterogeneity. Hebbian learning presents a biologically inspired form of neural adaptation that solely relies on local information. By doing so, we resolve several major challenges for learning heterogeneous control: 1) Hebbian learning removes the complexity of attributing emergent phenomena to single agents through local learning rules, thus circumventing the micro-macro problem; 2) uniform Hebbian learning rules across all swarm members limit the number of parameters needed, mitigating the curse of dimensionality with scaling swarm sizes; and 3) evolving Hebbian learning rules based on swarm-level behaviour minimises the need for extensive prior knowledge typically required for optimising heterogeneous swarms. This work demonstrates that with Hebbian learning heterogeneity naturally emerges, resulting in swarm-level behavioural switching and in significantly improved swarm capabilities. It also demonstrates how the evolution of Hebbian learning rules can be a valid alternative to Multi Agent Reinforcement Learning in standard benchmarking tasks.

Problem

• Enabling automatic emergence of heterogeneity in swarm robotics
• Resolving micro-macro problem with local Hebbian learning rules
• Mitigating curse of dimensionality in scaling swarm sizes

Innovation

• Hebbian learning enables automatic swarm heterogeneity
• Local Hebbian rules reduce micro-macro complexity
• Uniform rules mitigate dimensionality curse in swarms