Transformer让机器人更懂环境、会规划、能交互。
Advances in Transformers for Robotic Applications: A Review
- 用自注意力机制提升机器人感知与决策能力
- 结合强化学习实现长时序任务规划与泛化
- 适合研究机器人智能与人机协作的学者
Transformer架构在深度学习中带来显著突破，尤其在自然语言处理领域。其自注意力机制和可扩展性使其优于诸多传统神经网络。本文综述了Transformer在机器人领域的应用进展，涵盖感知、规划与控制中的最新趋势。重点分析了Transformer作为预训练基础模型的使用，以及与深度强化学习（DRL）的融合，用于提升自主系统在可靠规划、人机交互、长时决策和泛化能力方面的表现。同时探讨了不同Transformer变体在机器人中的适应性，并指出当前局限与未来研究方向。
原文摘要 · Abstract (English)
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.
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