  The Transformer model, initially achieving significant success in the field
of natural language processing, has recently shown great potential in the
application of tactile perception. This review aims to comprehensively outline
the application and development of Transformers in tactile technology. We first
introduce the two fundamental concepts behind the success of the Transformer:
the self-attention mechanism and large-scale pre-training. Then, we delve into
the application of Transformers in various tactile tasks, including but not
limited to object recognition, cross-modal generation, and object manipulation,
offering a concise summary of the core methodologies, performance benchmarks,
and design highlights. Finally, we suggest potential areas for further research
and future work, aiming to generate more interest within the community, tackle
existing challenges, and encourage the use of Transformer models in the tactile
field.
