机器人集群通过通信实现边学边协调，提升整体表现。
Signaling and Social Learning in Swarms of Robots
- 采用分布式通信机制，同步学习与执行
- 通信能缓解个体贡献评估难题，改善协作效率
- 适合研究群体智能与自适应机器人系统的人参考
本文研究通信在提升机器人集群协调性中的作用，聚焦于一种学习与执行同时进行的去中心化范式。强调通信在解决信用分配问题（个体对整体性能的贡献）中的关键作用及其相互影响。提出一个基于信息选择与物理抽象的通信分类体系：从低层次无损压缩与原始信号处理，到高层次有损压缩与结构化通信模型。综述进化机器人、多智能体（深度）强化学习、语言模型及生物物理模型的研究进展，揭示机器人集体通过局部消息交换持续互学所面临的挑战与机遇，体现一种社会学习形式。
原文摘要 · Abstract (English)
This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.
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