Scholarlink

<p>Recent advancements in transformer-based systems, including Large Language Models and Large Vision Models, have significantly transformed robotic manipulation by enabling enhanced task planning, real-time decision-making, and adaptive behaviour in complex environments. This review synthesises current research on integrating these models with robotic control systems, highlighting innovative strategies that merge linguistic and visual processing to improve precision and efficiency. It also critically examines challenges such as scalability, robustness, interpretability, and real-world applicability while identifying research gaps and future directions. This paper provides a concise yet comprehensive overview of the transformative impact of transformer-based systems on robotics, offering valuable insights for developing more sophisticated and versatile robotic systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Review of Advances in Large Language and Vision Models for Robotic Manipulation: Techniques, Integrations, and Challenges

  • 期刊论文

  • 发表日期：

    2025年6月25日

查看全文
  • Sajjad Hussain,
  • Shwetangshu Biswas,
  • Amandip Dutta,
  • Md Saad,
  • Almas Baimagambetov,
  • Khizer Saeed,
  • Nikolaos Polatidis

摘要

Recent advancements in transformer-based systems, including Large Language Models and Large Vision Models, have significantly transformed robotic manipulation by enabling enhanced task planning, real-time decision-making, and adaptive behaviour in complex environments. This review synthesises current research on integrating these models with robotic control systems, highlighting innovative strategies that merge linguistic and visual processing to improve precision and efficiency. It also critically examines challenges such as scalability, robustness, interpretability, and real-world applicability while identifying research gaps and future directions. This paper provides a concise yet comprehensive overview of the transformative impact of transformer-based systems on robotics, offering valuable insights for developing more sophisticated and versatile robotic systems.