ASCII Art Turns LLMs into VLA Controllers

• URL: http://arxiv.org/abs/2606.21470v1
• Date: Fri, 19 Jun 2026 14:19:59 GMT
• Title: ASCII Art Turns LLMs into VLA Controllers
• Abstract summary: Vision--Language--Action (VLA) controllers are often built by extending vision--language models (VLMs) with action supervision.<n>We demonstrate that a text-only large language model (LLM) can be adapted into a VLA-style controller when visual observations are rendered into a text input.
• Score: 24.00781789697405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision--Language--Action (VLA) controllers are often built by extending vision--language models (VLMs) with action supervision, relying on multimodal backbones with large data and compute requirements. We demonstrate that a text-only large language model (LLM) can be adapted into a VLA-style controller when visual observations are rendered into a text input using an ASCII representation. This ASCII-as-vision interface enables existing training and deployment stacks for LLMs to efficiently condition on visual state, follow natural-language instructions, and produce constrained, executable actions. We fine-tune and compare multiple LLMs and VLMs across model families and scales, using both expert demonstrations from a planning-based teacher, as well as DAgger for iterative improvement. In a 2D manipulation benchmark, in both simulation and on a physical manipulator, the resulting controllers can identify task-relevant entities and plan feasible action sequences. Our results suggest that ASCII rendering can serve as a lightweight, interpretable modality bridge from images to text, complementing conventional VLA pipelines, and opening directions for VLA research with text-only backbones.

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