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.

翻译：本文研究了通信在提升机器人群体协调性中的作用，重点关注一种去中心化模式下学习与执行同步进行的范式。我们强调通信在解决信用分配问题（个体对整体性能的贡献）中可能发挥的作用，以及该问题如何反作用于通信机制。我们提出了一套针对现有及未来通信研究的分类体系，以信息选择与物理抽象作为主要分类维度：从基于原始信号提取处理的低层无损压缩，到采用结构化通信模型的高层有损压缩。本文综述了来自演化机器人学、多智能体（深度）强化学习、语言模型及生物物理学模型等领域的最新研究，以阐明在通过局部信息交换持续相互学习的机器人集群中通信所面临的挑战与机遇，这体现了一种社会学习的形式。