mlsanityData to 14 Oct 2026

Advances in Transformers for Robotic Applications: A Review

arXiv 2412.10599 ↗First posted 13 Dec 2024cs.RO cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 90 of its 139 references that are in the map. First posted on arXiv 13 Dec 2024.

Builds on

90 of 139 references.

References · 90

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38 more
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50 most cited of 90 All 90 (JSON)

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025End-to-End Analysis of Charge Stability Diagrams with Transformerscited by 3
  2. 2025Deep Learning Perspective of Scene Understanding in Autonomous Robotscited by 0

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  6. 2024What comes after transformers? -- A selective survey connecting ideas in deep learning
  7. 2023SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
  8. 2022TransDreamer: Reinforcement Learning with Transformer World Models