TurboVLA：在RTX 4090上以32Hz实时运行的视觉-语言-动作模型，显存占用小于1GB

摘要

视觉-语言-动作（VLA）模型通常采用以大语言模型为中心的“视觉→语言→动作”路径，即先将视觉观测投射到大语言模型的表征空间，再解码为机器人动作。尽管这一设计行之有效，但每次策略调用都会产生大量的计算和内存开销。为此，我们提出TurboVLA——一种全新的VLA范式，它将传统的“V→L→A”路径重构为直接的“V+L→A”映射。TurboVLA不再将大语言模型作为感知与动作之间的核心接口，而是独立编码视觉观测和语言指令，通过轻量化的双向视觉-语言交互实现信息直接交换，并使用紧凑的解码器预测连续的动作块。这种简洁的设计直接从视觉和语言特征中构建任务条件表征，显著降低了VLA推理的计算和内存成本。在LIBERO基准上，TurboVLA仅以0.2B参数、31.2毫秒推理延迟和0.9GB推理显存（搭载消费级RTX 4090）便达到了97.7%的平均成功率，与显著更大规模的VLA策略相当甚至更优。这些结果表明TurboVLA是一种简洁而有效的替代方案，挑战了当前主流的以大语言模型为中心的VLA范式，为如何高效连接视觉、语言与动作以实现机器人操作提供了全新视角。代码已开源：https://github.com/H-EmbodVis/TurboVLA。

English

Vision-language-action (VLA) models commonly adopt an LLM-centric V to L to A pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation. In this work, we introduce TurboVLA, a new VLA paradigm that reformulates the conventional V to L to A pathway as a direct V + L to A mapping. Instead of using a large language model as the central interface between perception and action, TurboVLA independently encodes visual observations and language instructions, directly exchanges information between them through lightweight bidirectional vision-language interaction, and predicts continuous action chunks with a compact decoder. This simple design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference. On LIBERO, TurboVLA achieves 97.7% average success with only 0.2B parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger VLA policies. These results establish TurboVLA as a simple and effective alternative to the prevailing LLM-centric VLA paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation. Code is available at https://github.com/H-EmbodVis/TurboVLA.