State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey

TL;DR

This survey reviews recent advances in robot learning for multi-robot cooperation, discussing methods inspired by biological systems, their challenges, and future trends in integrating AI with robotic systems for real-world applications.

Contribution

It provides a comprehensive overview of current robot learning techniques for multi-robot systems, highlighting advantages, disadvantages, and technical challenges.

Findings

Statistical methods validate key ideas discussed.

Biologically inspired learning frameworks are prominent.

Emerging trends suggest increased AI integration in MRS.

Abstract

With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad application possibilities for MRS. This article surveys the state-of-the-art of robot learning in the context of Multi-Robot Cooperation (MRC) of recent. Commonly adopted robot learning methods (or frameworks) that are inspired by humans and animals are reviewed and their advantages and disadvantages are discussed along with the associated technical challenges. The potential trends of robot learning and MRS integration exploiting the merging of these methods with real-world applications is also discussed at length. Specifically statistical methods are used to quantitatively corroborate the ideas elaborated in the article.