用赫布学习让机器人集群自动产生差异，提升整体智能。
Emergent Heterogeneous Swarm Control Through Hebbian Learning
- 基于局部信息的赫布学习，实现无中心控制的集群分化。
- 集群行为切换能力提升，任务完成率显著优于传统方法。
- 无需预设规则，适合大规模复杂场景下的自适应集群控制。
本文提出将赫布学习应用于群体机器人系统，实现异质性自主涌现。该方法基于生物启发的神经适应机制，仅依赖局部信息，有效解决异质控制学习中的三大难题：1）通过局部学习规则避免微观-宏观映射困境；2）所有成员采用统一赫布规则，降低参数数量，缓解规模扩展时的维度灾难；3）基于集群行为演化学习规则，减少对先验知识的依赖。实验表明，赫布学习可自然催生异质性，带来集群行为切换与显著增强的任务能力，并在标准基准任务中表现媲美多智能体强化学习，为群体智能提供新范式。
原文摘要 · Abstract (English)
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.
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