We present MiniVLA-Nav v1, a simulation dataset for Language-Conditioned Object Approach (LCOA) navigation: given a short natural-language instruction, an NVIDIA Nova Carter differential-drive robot must navigate to the named object and stop within 1 m across four photorealistic Isaac Sim environments (Office, Hospital, Full Warehouse, and Warehouse with Multiple Shelves). Each of the 1,174 episodes pairs an instruction with synchronized 640x640 RGB images, metric depth maps (float32, metres), and instance segmentation masks, together with continuous (v,omega) and 7x7 tokenized expert action labels recorded at 60 Hz from a vision-based proportional controller. Trajectory diversity is ensured through three spawn-distance tiers (near: 1.5-3.5 m, mid: 3.5-7.0 m, far: global curated points; Pearson r=0.94 between spawn distance and trajectory length), 12 object categories, 18 training templates, and 12 paraphrase-OOD templates. Five evaluation splits support in-distribution accuracy, template-paraphrase robustness, and OOD object-category benchmarking. The dataset is publicly available at https://huggingface.co/datasets/alibustami/miniVLA-Nav

翻译：我们提出MiniVLA-Nav v1，这是一个为语言条件目标接近（LCOA）导航设计的仿真数据集：给定一条简短的自然语言指令，搭载NVIDIA Nova Carter的差速驱动机器人需导航至指定物体并在一米范围内停止，涵盖四个逼真的Isaac Sim场景（办公室、医院、完整仓库及多层货架仓库）。全部1,174组任务中，每组均将指令与同步的640×640 RGB图像、度量深度图（float32，单位米）及实例分割掩码配对，同时附有基于视觉比例控制器在60 Hz频率下记录的连续（v,ω）及7×7离散化专家动作标签。通过三档生成距离（近程：1.5-3.5米、中程：3.5-7.0米、远程：全局精选点；生成距离与轨迹长度间的皮尔逊相关系数r=0.94）、12种物体类别、18种训练模板及12种释义-分布外测试模板确保轨迹多样性。五个评估切分支持分布内准确率、模板-释义鲁棒性及分布外物体类别基准测试。该数据集公开获取地址为https://huggingface.co/datasets/alibustami/miniVLA-Nav