研究人员开发了RhinoVLA，一个用于边缘硬件上实时机器人操作的视觉-语言-动作（Vision-Language-Action）模型。该模型利用了令牌效率高的Qwen3-VL骨干网络和一个连续动作专家（continuous Action Expert）来降低计算负载和延迟。RhinoVLA还引入了一个统一的接口用于跨机器人学习，并针对硬件部署进行了优化，在满足10 Hz实时控制目标的同时，实现了与现有模型相当的下游性能。 AI
影响 可在边缘设备上实现实时机器人操作，可能加速自主系统。
排序理由 该集群包含一份技术报告，详细介绍了新模型及其在特定硬件上的性能。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging. In this work, we identify VLM visual and context tokens as a major source of …
Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging. In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection …