The introduction of Transformers architecture has brought about significant breakthroughs in Deep Learning (DL), particularly within Natural Language Processing (NLP). Since their inception, Transformers have outperformed many traditional neural network architectures due to their "self-attention" mechanism and their scalability across various applications. In this paper, we cover the use of Transformers in Robotics. We go through recent advances and trends in Transformer architectures and examine their integration into robotic perception, planning, and control for autonomous systems. Furthermore, we review past work and recent research on use of Transformers in Robotics as pre-trained foundation models and integration of Transformers with Deep Reinforcement Learning (DRL) for autonomous systems. We discuss how different Transformer variants are being adapted in robotics for reliable planning and perception, increasing human-robot interaction, long-horizon decision-making, and generalization. Finally, we address limitations and challenges, offering insight and suggestions for future research directions.

翻译：Transformer架构的引入为深度学习（DL）带来了重大突破，尤其是在自然语言处理（NLP）领域。自问世以来，凭借其“自注意力”机制以及在各种应用中的可扩展性，Transformer已超越了许多传统的神经网络架构。本文综述了Transformer在机器人学中的应用。我们梳理了Transformer架构的最新进展与趋势，并探讨了其在自主系统的机器人感知、规划与控制中的集成。此外，我们回顾了将Transformer作为预训练基础模型应用于机器人学的研究工作，以及Transformer与深度强化学习（DRL）在自主系统中的结合。我们讨论了不同Transformer变体如何被适配于机器人领域，以实现可靠的规划与感知、增强人机交互、进行长时程决策以及提升泛化能力。最后，我们分析了现有局限与挑战，并对未来研究方向提出了见解与建议。