机器人AI瘦身成功：0.2B参数跑赢大模型
现在的机器人操控AI普遍走「大语言模型当大脑」的路子：先让视觉信息挤进语言模型的语义空间，再翻译成动作。这就像每次动手前都要先过一遍翻译官，又慢又占内存。TurboVLA反着来——视觉和语言各走各的，中间只开两扇小窗交换信息，然后直接输出动作。结果：0.2B参数（比主流小一个数量级），在普通RTX 4090上跑出97.7%成功率，延迟31毫秒，显存不到1GB。它不是你明天就能用的产品，但意味着「机器人AI跑在本地小设备上」从实验室幻想变成了工程现实。
📄 原文摘要(英文)
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.