Polish bilingual dataset overview and training guidance

Add aggregate episode, synchronized-time-step and camera-video totals; clarify the automatic HF video preview; refine source and training wording while preserving tables, task definitions, visual assets and tested code.

README.md

@@ -45,13 +45,15 @@ RoboValue provides expert demonstrations for **robotic value models**, covering
 | ▦ **Full-Shot** | 100 per task | Training or fine-tuning on the complete expert collection |
 | ◉ **One-Shot** | 1 per task | Reference conditioning or task-specific adaptation |
 
-The two One-Shot subsets contain **35 episodes, 23,747 time steps, and 105 camera videos** in total. These are standard in-domain expert demonstrations; value or progress supervision is constructed by the training method.
+Across all four subsets, the release contains **3,535 episodes, 2,344,346 synchronized time steps, and 10,605 camera videos**. The two One-Shot subsets account for **35 episodes, 23,747 time steps, and 105 camera videos**.
+
+> **HF preview:** The automatic preview counts video files as rows, including one showcase clip. Use the episode and time-step totals here and the [LeRobot loader](#download-and-reading) for training.
 
 ## 🟦 2. Simulation <a name="simulation"></a>
 
 ### 2.1 Overview
 
-Simulation demonstrations use the **ARX** robot platform. They are based on the open-source [**RoboDojo**](https://huggingface.co/datasets/RoboDojo-Benchmark/RoboDojo) dataset, reviewed by our team with selected demonstrations recollected. They cover **15 manipulation tasks** at **25 FPS**.
+Simulation demonstrations use the **ARX** robot platform. They are based on the open-source [**RoboDojo**](https://huggingface.co/datasets/RoboDojo-Benchmark/RoboDojo) dataset. Our team reviewed the data and recorded selected demonstrations again. They cover **15 manipulation tasks** at **25 FPS**.
 
 **What does one trajectory contain?**
 
@@ -309,7 +311,7 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
 prefix = "data/simulation/one-shot"
-revision = "95878d95f657f23d78a396faa4c000f935a783f9"
+revision = "faeed578f63b3fdc95794172d495560a29b043d9"
 local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
 
 snapshot = snapshot_download(
@@ -364,7 +366,11 @@ instructions = batch["task"]
 print("batch action / state:", tuple(actions.shape), tuple(states.shape))
 ```
 
-Simulation One-Shot prints `batch action / state: (2, 14) (2, 14)`. For real-world data, the corresponding shapes are `(2, 52)` and `(2, 290)`. The batch supplies expert states, actions, task instructions, and three camera views. Value or progress targets are constructed by the training method. Configure the model's input/output dimensions for the selected domain. Each time step is sampled as one training item; sequence-based training uses LeRobot `delta_timestamps`.
+Simulation One-Shot prints `batch action / state: (2, 14) (2, 14)`. For real-world data, the corresponding shapes are `(2, 52)` and `(2, 290)`.
+
+Each batch contains states, actions, task instructions, and three camera views. Configure the model's input and output dimensions for the selected domain. The training method constructs value or progress targets from these expert demonstrations.
+
+Each time step is one training item. For temporal windows, configure LeRobot `delta_timestamps`.
 
 ### 5.4 Integrity checks
 

README_zh.md

@@ -28,7 +28,9 @@ RoboValue 提供面向**机器人价值模型**的专家示例，覆盖 **15 个
 | ▦ **Full-Shot** | 每任务 100 条 | 使用完整专家示例进行训练或微调 |
 | ◉ **One-Shot** | 每任务 1 条 | 以单条任务示例进行参考条件输入或任务适配 |
 
-两个 One-Shot 子集合计 **35 条轨迹、23,747 个时间步、105 个相机视频**。本数据集提供标准域内专家示例；价值或进度监督由训练方法构造。
+四个子集合计 **3,535 条轨迹、2,344,346 个同步时间步、10,605 个相机视频**，其中 One-Shot 合计 **35 条轨迹、23,747 个时间步、105 个相机视频**。
+
+> **HF 预览说明：**自动预览按视频文件计数，其中包含 1 个展示视频。训练规模请查看这里的轨迹数与时间步数，并按[LeRobot 读取示例](#download-and-reading)加载数据。
 
 ## 🟦 2. 仿真世界 <a name="simulation"></a>
 
@@ -292,7 +294,7 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
 prefix = "data/simulation/one-shot"
-revision = "95878d95f657f23d78a396faa4c000f935a783f9"
+revision = "faeed578f63b3fdc95794172d495560a29b043d9"
 local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
 
 snapshot = snapshot_download(
@@ -347,7 +349,11 @@ instructions = batch["task"]
 print("batch action / state:", tuple(actions.shape), tuple(states.shape))
 ```
 
-仿真 One-Shot 输出 `batch action / state: (2, 14) (2, 14)`；真机数据对应为 `(2, 52)` 和 `(2, 290)`。batch 提供专家状态、动作、任务指令和三路相机观测，value 或 progress 目标由训练方法构造。模型输入输出维度按所选领域配置；默认每个时间步作为一个训练样本，时序训练使用 LeRobot 的 `delta_timestamps`。
+仿真 One-Shot 输出 `batch action / state: (2, 14) (2, 14)`；真机数据对应为 `(2, 52)` 和 `(2, 290)`。
+
+batch 包含状态、动作、任务指令与三路相机观测。模型的输入输出维度应匹配所选领域；价值（value）或进度（progress）监督目标由训练方法根据专家示范构造。
+
+默认每个时间步作为一个训练样本；时序窗口通过 LeRobot 的 `delta_timestamps` 配置。
 
 ### 5.4 完整性检查
 

subset_catalog.json

@@ -61,7 +61,7 @@
       ],
       "videos": 4500,
       "platform": "ARX",
-      "source_description_en": "Based on open-source RoboDojo data, reviewed by our team with selected demonstrations recollected.",
+      "source_description_en": "Based on open-source RoboDojo data reviewed by our team, with selected demonstrations recorded again.",
       "source_description_zh": "基于 RoboDojo 开源数据，经团队审核后，对部分示例重新采集。",
       "display_task_names": [
         "arrange_largest_number",
@@ -108,7 +108,7 @@
       "episode_mapping": "data/simulation/one-shot/provenance/episode_mapping.json",
       "videos": 45,
       "platform": "ARX",
-      "source_description_en": "Based on open-source RoboDojo data, reviewed by our team with selected demonstrations recollected.",
+      "source_description_en": "Based on open-source RoboDojo data reviewed by our team, with selected demonstrations recorded again.",
       "source_description_zh": "基于 RoboDojo 开源数据，经团队审核后，对部分示例重新采集。",
       "display_task_names": [
         "arrange_largest_number",
@@ -188,6 +188,6 @@
     }
   ],
   "planned_subsets": [],
-  "data_revision": "95878d95f657f23d78a396faa4c000f935a783f9",
+  "data_revision": "faeed578f63b3fdc95794172d495560a29b043d9",
   "publication_authorization": "All data were authorized before public release."
 }