Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, ISIR, F-75005 Paris, France.
Philos Trans R Soc Lond B Biol Sci. 2022 Jan 31;377(1843):20200309. doi: 10.1098/rstb.2020.0309. Epub 2021 Dec 13.
In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performed on a local basis. While some issues are specific to artificial systems, such as the general objective of learning efficient (and ideally, optimal) behavioural strategies to fulfill a task defined by a supervisor, some other issues are shared with social learning in natural systems. We discuss some of these issues, paving the way towards cumulative cultural evolution in robot swarms, which could enable complex social organization necessary to achieve challenging robotic tasks. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'.
本文提出了一种群体机器人的社会学习实现方法。我们将社会学习视为一种分布式在线强化学习方法,应用于一个机器人集体中,其中感知、行动和协调都是在本地进行的。虽然有些问题是人工系统特有的,例如学习高效(理想情况下是最优)行为策略来完成由监督者定义的任务的一般目标,但还有一些问题与自然系统中的社会学习共享。我们讨论了其中的一些问题,为机器人群体中的累积文化进化铺平了道路,这将使实现具有挑战性的机器人任务所需的复杂社会组织成为可能。本文是“动物、人类和机器中集体知识和累积文化的出现”讨论专题的一部分。