Cazenille Leo, Toquebiau Maxime, Lobato-Dauzier Nicolas, Loi Alessia, Macabre Loona, Aubert-Kato Nathanaël, Genot Anthony J, Bredeche Nicolas
Universite Paris Cite, CNRS, LIED UMR 8236, Paris F-75006, France.
Sorbonne Universite, CNRS, ISIR, Paris F-75005, France.
Philos Trans A Math Phys Eng Sci. 2025 Jan 30;383(2289):20240148. doi: 10.1098/rsta.2024.0148.
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.This article is part of the theme issue 'The road forward with swarm systems'.
本文研究了通信在改善机器人集群内部协调方面的作用，重点关注一种学习和执行以分散方式同时进行的范式。我们强调了通信在解决信用分配问题（个体对整体性能的贡献）中可以发挥的作用，以及它如何受到该问题的影响。我们提出了一个关于现有和未来通信工作的分类法，重点将信息选择和物理抽象作为分类的主要轴：从具有原始信号提取和处理的低级无损压缩到具有结构化通信模型的高级有损压缩。本文回顾了来自进化机器人学、多智能体（深度）强化学习、语言模型和生物物理学模型的当前研究，以概述在通过局部消息交换相互持续学习的机器人集群中通信的挑战和机遇，展示了一种社会学习形式。本文是主题特刊“群体系统的前进之路”的一部分。
