State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey
This is an incremental survey paper for researchers in robotics and AI.
This survey reviews state-of-the-art robot learning methods for multi-robot collaboration, analyzing their advantages, disadvantages, and technical challenges while using statistical methods to quantitatively support the discussion.
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.