Remove auxiliary source identifiers from training release

Remove source trajectory/task mappings and refresh bookkeeping; retain local LeRobot indices, conversion facts, task instructions, annotations, and bilingual documentation. Update checksums and catalog; preserve all data/video/annotation bytes and evaluation files.

README.md

@@ -222,7 +222,7 @@ Loaded images use **CHW (channels, height, width)** order.
 | ---: | --- | ---: | ---: | --- |
 | **8,080** | **45 · AV1** | **≈0.50 GB** | ≈5.39 minutes | `train: 0:15` |
 
-[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/one-shot) · [Task–episode–row mapping](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/provenance/episode_mapping.json) · [Download and read](#download-and-reading)
+[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/one-shot) · [Download and read](#download-and-reading)
 
 **Subtask annotations:** The separate adaptation supplement retains **58 subtask segments and 8,080 frame labels** in `annotations/subtasks.json`, `annotations/subtask_segments.parquet`, and `annotations/frame_labels.parquet`. These files are separate from training model observations and test-set labels; reading instructions are linked above.
 
@@ -355,13 +355,11 @@ Loaded images use **CHW (channels, height, width)** order.
 
 **20 tasks × 1 demonstration = 20 episodes.** Uses the same task definitions as Full-Shot.
 
-For 19 tasks, the selected trajectory has the **smallest original `episode_id` among the task's 100 Full-Shot source trajectories**. **Arrange Family Tea Set** uses **`52375079`**, the smallest original ID in the **27-episode tea refresh batch**. Original IDs and source conversion indices are recorded separately in the mapping below.
-
 | ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
 | ---: | --- | ---: | ---: | --- |
 | **16,362** | **60 · HEVC** | **≈0.22 GB** | ≈9.09 minutes | `train: 0:20` |
 
-[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/one-shot) · [Task–episode–row mapping](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/provenance/episode_mapping.json) · [Download and read](#download-and-reading)
+[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/one-shot) · [Download and read](#download-and-reading)
 
 ## 🗂️ 4. Repository Layout <a name="repository-layout"></a>
 
@@ -404,7 +402,7 @@ The repository-level training directory is `training-data/`. Each LeRobot root r
 | `meta/tasks.parquet` | Task instructions and indices |
 | `meta/episodes/chunk-*/file-*.parquet` | Episode length, task, data-row boundaries, video offsets, and episode statistics |
 | `meta/stats.json` | Dataset-level feature statistics |
-| `provenance/` | Episode sources and task correspondence |
+| `meta/conversion.json` | Data conversion, video handling, and statistics notes |
 | `checksums.sha256` | File integrity manifest |
 
 Data rows span `[dataset_from_index, dataset_to_index)`; video segments are recorded separately for each camera. Time steps and duration are counted once per episode.
@@ -447,7 +445,7 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
 prefix = "training-data/simulation/one-shot"
-revision = "ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e"
+revision = "main"  # Pin the current commit hash for reproducible runs.
 local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
 
 snapshot = snapshot_download(
@@ -481,7 +479,7 @@ observation.images.cam_left_wrist (3, 480, 640)
 observation.images.cam_right_wrist (3, 480, 640)
 ```
 
-Files are saved under `./robovalue-data`. Set the `ROBOVALUE_LOCAL_DIR` environment variable to an existing download directory to reuse local files. Download and reading use the same fixed data revision.
+Files are saved under `./robovalue-data`. Set the `ROBOVALUE_LOCAL_DIR` environment variable to an existing download directory to reuse local files. Download and reading use the same `revision`; pin the current commit hash for reproducible runs.
 
 
 ### 5.3 Use a training batch
@@ -520,7 +518,7 @@ shasum -a 256 -c checksums.sha256
 
 ### 5.5 Download and read a public test subset
 
-Choose `evaluation/real-world/test-30` or `evaluation/simulation/test-30` to download one public test subset. The example is pinned to `578593ef28ee7a2d861ebf38162c928291b0d4a9`, which contains both test domains; downloading and loading use the same revision. Training examples use the separately pinned revision containing the `training-data/` directory.
+Choose `evaluation/real-world/test-30` or `evaluation/simulation/test-30` to download one public test subset. The example is pinned to `578593ef28ee7a2d861ebf38162c928291b0d4a9`, which contains both test domains; downloading and loading use the same revision. Training examples use the current `main` revision; pin its commit hash for reproducible runs.
 
 ```python
 from pathlib import Path

README_zh.md

@@ -193,7 +193,7 @@ Full-Shot 与 One-Shot 中，每条 **episode** 都是一条完整的任务示
 | ---: | --- | ---: | ---: | --- |
 | **8,080** | **45 个 · AV1** | **≈0.50 GB** | ≈5.39 分钟 | `train: 0:15` |
 
-[**浏览文件 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/one-shot) · [任务—轨迹—行范围映射](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/provenance/episode_mapping.json) · [下载与读取](#download-and-reading)
+[**浏览文件 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/one-shot) · [下载与读取](#download-and-reading)
 
 **子任务标注：**独立的适配补标保留 **58个子任务区间、8,080条逐帧标签**，使用统一的 `annotations/subtasks.json`、`annotations/subtask_segments.parquet` 和 `annotations/frame_labels.parquet`。文件独立于训练模型观测与测试集标签；读取方法见上方子任务说明。
 
@@ -326,13 +326,11 @@ Full-Shot 与 One-Shot 中，每条 **episode** 都是一条完整的任务示
 
 **20 个任务 × 1 条 = 20 条轨迹。** 与 Full-Shot 使用同一组任务定义。
 
-其余 19 个任务选择其 100 条 Full-Shot 源轨迹中**原始 `episode_id` 最小的一条**；**摆放家庭茶具**选择本次 **27 条茶席轨迹中原始 ID 最小的 `52375079`**。原始 ID 与源转换索引分别记录在下方映射中。
-
 | ▦ 时间步 | ▶ 视频 | ▣ 大小 | ◷ 总时长 | ⑂ 训练划分 |
 | ---: | --- | ---: | ---: | --- |
 | **16,362** | **60 个 · HEVC** | **≈0.22 GB** | ≈9.09 分钟 | `train: 0:20` |
 
-[**浏览文件 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/one-shot) · [任务—轨迹—行范围映射](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/provenance/episode_mapping.json) · [下载与读取](#download-and-reading)
+[**浏览文件 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/one-shot) · [下载与读取](#download-and-reading)
 
 ## 🗂️ 4. 仓库目录 <a name="repository-layout"></a>
 
@@ -375,7 +373,7 @@ evaluation/
 | `meta/tasks.parquet` | 任务指令及索引 |
 | `meta/episodes/chunk-*/file-*.parquet` | 轨迹长度、任务、数据行边界、视频偏移与逐轨迹统计 |
 | `meta/stats.json` | 数据集级别的字段统计 |
-| `provenance/` | 轨迹来源与任务对应关系 |
+| `meta/conversion.json` | 数据转换、视频处理与统计说明 |
 | `checksums.sha256` | 文件完整性清单 |
 
 数据行范围为 `[dataset_from_index, dataset_to_index)`；各相机的视频时间段分别记录。时间步与时长按每条轨迹统计一次。
@@ -418,7 +416,7 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
 prefix = "training-data/simulation/one-shot"
-revision = "ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e"
+revision = "main"  # Pin the current commit hash for reproducible runs.
 local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
 
 snapshot = snapshot_download(
@@ -452,7 +450,7 @@ observation.images.cam_left_wrist (3, 480, 640)
 observation.images.cam_right_wrist (3, 480, 640)
 ```
 
-文件保存在 `./robovalue-data`。设置环境变量 `ROBOVALUE_LOCAL_DIR` 可指定已有下载目录，复用本地文件。下载与读取采用同一个固定数据版本。
+文件保存在 `./robovalue-data`。设置环境变量 `ROBOVALUE_LOCAL_DIR` 可指定已有下载目录，复用本地文件。下载与读取采用相同的 `revision`；需要复现时固定当前版本的提交哈希。
 
 
 ### 5.3 用于模型训练
@@ -491,7 +489,7 @@ shasum -a 256 -c checksums.sha256
 
 ### 5.5 下载并读取公开测试子集
 
-选择 `evaluation/real-world/test-30` 或 `evaluation/simulation/test-30`，仅下载其中一个公开测试子集。下方示例固定到包含两个测试集的版本 `578593ef28ee7a2d861ebf38162c928291b0d4a9`，下载和加载使用同一提交。训练示例另行固定到包含 `training-data/` 新目录的提交。
+选择 `evaluation/real-world/test-30` 或 `evaluation/simulation/test-30`，仅下载其中一个公开测试子集。下方示例固定到包含两个测试集的版本 `578593ef28ee7a2d861ebf38162c928291b0d4a9`，下载和加载使用同一提交。训练示例采用当前 `main` 版本；需要复现时固定该版本的提交哈希。
 
 ```python
 from pathlib import Path

SUBTASKS.md

@@ -17,7 +17,7 @@ Each of the six dataset roots has **`annotations/subtasks.json`** (schema v2). I
 
 ## Read test definitions and intervals
 
-The following example downloads just the definition and interval files, then finds the annotated subtask at frame 100 of real-world episode 0. Change `prefix` to the simulation test root to read its labels. An uncovered frame returns no label. Use the fixed revision shown below for downloading and loading.
+The following example downloads just the definition and interval files, then finds the annotated subtask at frame 100 of real-world episode 0. Change `prefix` to the simulation test root to read its labels. An uncovered frame returns no label. Use the same revision for downloading and loading; pin the current commit hash for reproducible runs.
 
 ```python
 import json
@@ -26,7 +26,7 @@ import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
-revision = "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
+revision = "main"  # Pin the current commit hash for reproducible runs.
 prefix = "evaluation/real-world/test-30"
 snapshot = snapshot_download(
     repo_id=repo_id, repo_type="dataset", revision=revision,

SUBTASKS_zh.md

@@ -17,7 +17,7 @@
 
 ## 读取测试定义与区间
 
-以下示例只下载定义和区间文件，查询真机第0条轨迹第100帧的子任务。将 `prefix` 改为仿真测试根目录即可读取其已有标签；未覆盖的帧返回空结果。下载与读取使用下方同一固定版本。
+以下示例只下载定义和区间文件，查询真机第0条轨迹第100帧的子任务。将 `prefix` 改为仿真测试根目录即可读取其已有标签；未覆盖的帧返回空结果。下载与读取采用相同的版本；需要复现时固定当前版本的提交哈希。
 
 ```python
 import json
@@ -26,7 +26,7 @@ import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download
 
 repo_id = "wzlllong/value-model-benchmark-training-data"
-revision = "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
+revision = "main"  # Pin the current commit hash for reproducible runs.
 prefix = "evaluation/real-world/test-30"
 snapshot = snapshot_download(
     repo_id=repo_id, repo_type="dataset", revision=revision,

subset_catalog.json

@@ -53,10 +53,6 @@
         "train": "0:1500"
       },
       "reference_repo": "RoboDojo-Benchmark/RoboDojo",
-      "source_episodes": {
-        "RoboDojo": 1393,
-        "supplemental": 107
-      },
       "video_codecs": [
         "av1",
         "h264"
@@ -82,23 +78,6 @@
         "Swap Blocks",
         "Sweep Blocks"
       ],
-      "source_task_keys": [
-        "arrange_largest_number",
-        "fill_pen_holder",
-        "fold_clothes",
-        "hang_mugs",
-        "insert_tubes",
-        "make_toast",
-        "organize_table",
-        "pack_objects_into_box",
-        "play_stacking_toy",
-        "press_by_number",
-        "put_bottles_into_dustbin",
-        "stack_blocks",
-        "store_laptop_and_headphones",
-        "swap_blocks",
-        "sweep_blocks"
-      ],
       "display_task_names_zh": [
         "排列数字组成最大数",
         "装填笔筒",
@@ -117,7 +96,7 @@
         "清扫积木"
       ],
       "role": "training",
-      "size_bytes": 45468427910,
+      "size_bytes": 45467875256,
       "size_scope": "entire subset directory, including annotations, documentation and integrity files",
       "subtasks": {
         "definitions": "training-data/simulation/full-shot/annotations/subtasks.json",
@@ -125,7 +104,25 @@
         "definition_count": 58,
         "temporal_annotations_available": false,
         "revision": "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
-      }
+      },
+      "task_keys": [
+        "arrange_largest_number",
+        "fill_pen_holder",
+        "fold_clothes",
+        "hang_mugs",
+        "insert_tubes",
+        "make_toast",
+        "organize_table",
+        "pack_objects_into_box",
+        "play_stacking_toy",
+        "press_by_number",
+        "put_bottles_into_dustbin",
+        "stack_blocks",
+        "store_laptop_and_headphones",
+        "swap_blocks",
+        "sweep_blocks"
+      ],
+      "conversion": "training-data/simulation/full-shot/meta/conversion.json"
     },
     {
       "subset_id": "simulation-one-shot-data",
@@ -151,7 +148,6 @@
       "video_codecs": [
         "av1"
       ],
-      "episode_mapping": "training-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 recorded again.",
@@ -173,23 +169,6 @@
         "Swap Blocks",
         "Sweep Blocks"
       ],
-      "source_task_keys": [
-        "arrange_largest_number",
-        "fill_pen_holder",
-        "fold_clothes",
-        "hang_mugs",
-        "insert_tubes",
-        "make_toast",
-        "organize_table",
-        "pack_objects_into_box",
-        "play_stacking_toy",
-        "press_by_number",
-        "put_bottles_into_dustbin",
-        "stack_blocks",
-        "store_laptop_and_headphones",
-        "swap_blocks",
-        "sweep_blocks"
-      ],
       "display_task_names_zh": [
         "排列数字组成最大数",
         "装填笔筒",
@@ -208,7 +187,7 @@
         "清扫积木"
       ],
       "role": "training",
-      "size_bytes": 499651539,
+      "size_bytes": 499637016,
       "size_scope": "entire subset directory, including annotations, documentation and integrity files",
       "subtasks": {
         "definitions": "training-data/simulation/one-shot/annotations/subtasks.json",
@@ -227,7 +206,25 @@
         "interval_convention": "[start_frame,end_frame)",
         "annotation_status": "user_confirmed_release",
         "boundary_mask": "boundary_uncertain"
-      }
+      },
+      "task_keys": [
+        "arrange_largest_number",
+        "fill_pen_holder",
+        "fold_clothes",
+        "hang_mugs",
+        "insert_tubes",
+        "make_toast",
+        "organize_table",
+        "pack_objects_into_box",
+        "play_stacking_toy",
+        "press_by_number",
+        "put_bottles_into_dustbin",
+        "stack_blocks",
+        "store_laptop_and_headphones",
+        "swap_blocks",
+        "sweep_blocks"
+      ],
+      "conversion": "training-data/simulation/one-shot/meta/conversion.json"
     },
     {
       "subset_id": "real-world-full-shot-data",
@@ -301,7 +298,7 @@
         "插花与摆放藤条香薰",
         "准备早餐"
       ],
-      "size_bytes": 23126365226,
+      "size_bytes": 23125731832,
       "duration_seconds": 51144.03333333333,
       "role": "training",
       "size_scope": "entire subset directory, including annotations, documentation and integrity files",
@@ -311,7 +308,8 @@
         "definition_count": 97,
         "temporal_annotations_available": false,
         "revision": "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
-      }
+      },
+      "conversion": "training-data/real-world/full-shot/meta/conversion.json"
     },
     {
       "subset_id": "real-world-one-shot-data",
@@ -337,11 +335,9 @@
       "video_codecs": [
         "hevc"
       ],
-      "episode_mapping": "training-data/real-world/one-shot/provenance/episode_mapping.json",
-      "source_episode_selection": "minimum original episode_id among the 100 source episodes for 19 tasks; tea uses the minimum original episode_id from the 27-episode tea refresh batch",
       "videos": 60,
       "platform": "AgiBot Genie02",
-      "size_bytes": 215695670,
+      "size_bytes": 215659523,
       "duration_seconds": 545.4,
       "display_task_names": [
         "Transfer Blocks Using Plate",
@@ -387,17 +383,6 @@
         "插花与摆放藤条香薰",
         "准备早餐"
       ],
-      "source_episode_selection_exceptions": [
-        {
-          "task_index": 9,
-          "display_task_name": "Arrange Family Tea Set",
-          "source_dataset_id": 224282,
-          "source_task_id": 172027,
-          "source_episode_index": 5,
-          "source_episode_id": 52375079,
-          "selection": "minimum original episode_id in the 27-episode tea refresh batch"
-        }
-      ],
       "role": "training",
       "size_scope": "entire subset directory, including annotations, documentation and integrity files",
       "subtasks": {
@@ -406,7 +391,8 @@
         "definition_count": 97,
         "temporal_annotations_available": false,
         "revision": "3f21eb1beeedaf1b1d5aaacda4a033174521d8c3"
-      }
+      },
+      "conversion": "training-data/real-world/one-shot/meta/conversion.json"
     },
     {
       "subset_id": "real-world-test-30",
@@ -608,14 +594,13 @@
     }
   ],
   "planned_subsets": [],
-  "data_revision": "ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e",
+  "data_revision": "main",
   "publication_authorization": "All data were authorized before public release.",
   "pretty_name": "RoboValue Training and Small-Scale Evaluation Data",
-  "training_data_revision": "ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e",
-  "revision_note": "training_data_revision identifies the unchanged training payloads under training-data/. source_training_data_revision records the original release under data/. evaluation_data_revision identifies the separate public test payloads.",
+  "training_data_revision": "main",
+  "revision_note": "Training metadata and documentation use main; pin the current repository commit for reproducibility. Evaluation payloads retain evaluation_data_revision.",
   "evaluation_data_revision": "578593ef28ee7a2d861ebf38162c928291b0d4a9",
-  "source_training_data_revision": "7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145",
-  "training_path_revision": "ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e",
+  "training_path_revision": "main",
   "metric_documentation": {
     "english": "README.md#metric-summary",
     "chinese": "README_zh.md#metric-summary",

training-data/real-world/full-shot/README.md

@@ -13,7 +13,7 @@ Expert demonstrations collected with the AgiBot Genie02 robot.
 | Synchronized time steps | 1,534,321 |
 | Sampling rate | 30 FPS |
 | Total duration | 51,144.0 seconds · ≈14.21 hours |
-| Training payload size | 23,125,415,964 bytes · ≈23.13 GB |
+| Training payload size | 23,124,730,898 bytes · ≈23.13 GB |
 | Robot state | `observation.state`: `float32[290]` |
 | Robot action | `action`: `float32[52]` |
 | RGB cameras | Head: 640 × 400; left/right wrist: 640 × 528 each |
@@ -51,11 +51,11 @@ This directory is an independent LeRobot dataset root. Set `root` to `training-d
 
 </details>
 
-Task indices match `meta/tasks.parquet`. Source episode identifiers and task correspondence are recorded in `provenance/`. `checksums.sha256` provides file integrity hashes.
+Task indices match `meta/tasks.parquet`. `checksums.sha256` provides file integrity hashes.
 
 **Publication authorization:** All data were authorized before public release.
 
-**Data revision:** `ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e`. Use this fixed revision with the download examples.
+**Data revision:** Download the current `main` revision; pin its commit hash for reproducible runs.
 
 ## 中文
 
@@ -67,9 +67,9 @@ Task indices match `meta/tasks.parquet`. Source episode identifiers and task cor
 
 下载后，将 LeRobot 的 `root` 设置为快照中的 `training-data/real-world/full-shot`。主卡片的[下载与训练示例](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md#download-and-reading)提供状态、动作、任务指令与三路相机观测的 PyTorch batch；value 或 progress 监督由训练方法构造。
 
-折叠表列出任务索引、轨迹分配及数据行范围；原始来源标识与对应关系保留在 `provenance/`。
+折叠表列出本子集的任务索引、轨迹分配及数据行范围。
 
-**数据版本：**`ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e`，下载与读取时使用该固定版本。
+**数据版本：**下载当前 `main` 版本；需要复现时固定该版本的提交哈希。
 
 **发布授权：**全部数据均经授权后公开发布。
 

training-data/real-world/full-shot/checksums.sha256

@@ -1,4 +1,4 @@
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+f736d7dc335f287a1bd70c3992e6a188704d533742cdf8d6596bbbe3d5286cc7  README.md
 1fa31a317ef6909abeef1ec3898009d63fd7bfeedaadafe60c2ec75c4ce214e1  annotations/subtasks.json
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training-data/real-world/full-shot/{provenance → meta}/conversion.json

@@ -19,15 +19,5 @@
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   "public_release_authorized": true,
   "license": "published with permission",
-  "trimmed_rows": 0,
-  "tea_refresh": {
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-    "source_manifest_sha256": "0817ff23698182080a32978d06c6236cd78dd70b73100dec477e1bcc3cae9a2c"
-  }
+  "trimmed_rows": 0
 }

training-data/real-world/full-shot/provenance/episodes.parquet

@@ -1,3 +0,0 @@
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training-data/real-world/full-shot/provenance/tea-refresh-20261021.json

@@ -1,309 +0,0 @@
-{
-  "schema_version": 1,
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-  "source_task_id": 172027,
-  "replacement_rule": "new batch original IDs ascending; first 20 pair with old minimum 20 by rank",
-  "one_shot_selection_exception": false,
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-    },
-    {
-      "rank": 20,
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-      "old_source_task_id": 19328,
-      "old_source_episode_id": 30123225,
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-      "old_length": 940,
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-      "new_source_task_id": 172027,
-      "new_source_episode_id": 52383876,
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-    }
-  ]
-}

training-data/real-world/one-shot/README.md

@@ -4,7 +4,7 @@
 
 **20 tasks × 1 demonstration per task = 20 episodes.** All episodes belong to the `train` split (`0:20`).
 
-Expert demonstrations collected with the **AgiBot Genie02** robot. For 19 tasks, the selected trajectory has the **smallest original `episode_id` among the task's 100 Full-Shot source trajectories**. **Arrange Family Tea Set** uses **`52375079`**, the smallest original ID in the **27-episode tea refresh batch**. The original ID is recorded separately from the source conversion `episode_index` and the target episode index.
+Expert demonstrations collected with the **AgiBot Genie02** robot, with one demonstration for each task.
 
 | Content | Specification |
 | --- | --- |
@@ -13,7 +13,7 @@ Expert demonstrations collected with the **AgiBot Genie02** robot. For 19 tasks,
 | Synchronized time steps | **16,362** |
 | Sampling rate | 30 FPS |
 | Total duration | 545.4 seconds · ≈9.09 minutes |
-| Training payload size | 215,642,018 bytes · ≈0.22 GB |
+| Training payload size | 215,613,913 bytes · ≈0.22 GB |
 | Robot state | `observation.state`: `float32[290]` |
 | Robot action | `action`: `float32[52]` |
 | RGB cameras | Head: 640 × 400; left/right wrist: 640 × 528 each |
@@ -21,83 +21,83 @@ Expert demonstrations collected with the **AgiBot Genie02** robot. For 19 tasks,
 
 This directory is an independent LeRobot dataset root. Set `root` to `training-data/real-world/one-shot` inside the downloaded snapshot. The [download and training examples](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data#download-and-reading) use LeRobot **0.4.2** and PyAV **15.1.0** and return states, actions, task instructions and three camera observations as PyTorch batches. Value or progress supervision is constructed by the training method.
 
-[Feature definitions](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/info.json) · [Shared trajectory contents and task descriptions](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data#real-world) · [Machine-readable source IDs and row ranges](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/provenance/episode_mapping.json)
+[Feature definitions](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/info.json) · [Shared trajectory contents and task descriptions](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data#real-world)
 
 <details>
-<summary><strong>Original IDs and task–episode–row mapping</strong></summary>
-
-| Task / target episode | Task / source directory | Original episode_id | Source episode_index | Row range [start, end) |
-| ---: | --- | ---: | ---: | --- |
-| 0 | Transfer Blocks Using Plate<br>`task_16249` | 24903119 | 0 | [0, 695) |
-| 1 | Enter Keypad Number Sequence<br>`task_16360` | 24657466 | 1 | [695, 1,301) |
-| 2 | Set the Table<br>`task_16924` | 24862628 | 0 | [1,301, 2,249) |
-| 3 | Arrange Seasoning Jars<br>`task_18495` | 24904426 | 78 | [2,249, 2,764) |
-| 4 | Load Plastic Plates Into Rack<br>`task_18678` | 24904665 | 62 | [2,764, 3,324) |
-| 5 | Press Button Five Times<br>`task_18806` | 24355082 | 18 | [3,324, 3,827) |
-| 6 | Place Pot On Induction Cooker<br>`task_18838` | 24907412 | 99 | [3,827, 4,746) |
-| 7 | Organize Desktop Workstation<br>`task_18880` | 24862694 | 8 | [4,746, 5,728) |
-| 8 | Organize Books And Pen<br>`task_18943` | 24899073 | 0 | [5,728, 6,638) |
-| 9 | Arrange Family Tea Set<br>`task_172027` | 52375079 | 5 | [6,638, 7,596) |
-| 10 | Operate Drawers In Sequence<br>`task_19366` | 24659167 | 0 | [7,596, 9,172) |
-| 11 | Sort Blocks in Order<br>`task_20141` | 24903542 | 0 | [9,172, 9,822) |
-| 12 | Fill And Place Pen Holder<br>`task_21687` | 29886369 | 41 | [9,822, 10,572) |
-| 13 | Organize Cosmetics Storage Box<br>`task_23555` | 24903450 | 0 | [10,572, 11,582) |
-| 14 | Strike Drum Five Times<br>`task_30403` | 24448503 | 41 | [11,582, 12,016) |
-| 15 | Apply Dish Soap Repeatedly<br>`task_30419` | 24907810 | 99 | [12,016, 12,575) |
-| 16 | Season Meat Skewers Repeatedly<br>`task_30462` | 26304238 | 99 | [12,575, 13,423) |
-| 17 | Pack Items For Shipping<br>`task_31119` | 24903840 | 4 | [13,423, 14,163) |
-| 18 | Arrange Flowers and Reed Diffuser<br>`task_31913` | 24905589 | 99 | [14,163, 15,307) |
-| 19 | Prepare Breakfast<br>`task_37438` | 24903652 | 8 | [15,307, 16,362) |
+<summary><strong>Task–episode–row mapping</strong></summary>
+
+| Task / episode index | Task | Row range [start, end) |
+| ---: | --- | --- |
+| 0 | Transfer Blocks Using Plate | [0, 695) |
+| 1 | Enter Keypad Number Sequence | [695, 1,301) |
+| 2 | Set the Table | [1,301, 2,249) |
+| 3 | Arrange Seasoning Jars | [2,249, 2,764) |
+| 4 | Load Plastic Plates Into Rack | [2,764, 3,324) |
+| 5 | Press Button Five Times | [3,324, 3,827) |
+| 6 | Place Pot On Induction Cooker | [3,827, 4,746) |
+| 7 | Organize Desktop Workstation | [4,746, 5,728) |
+| 8 | Organize Books And Pen | [5,728, 6,638) |
+| 9 | Arrange Family Tea Set | [6,638, 7,596) |
+| 10 | Operate Drawers In Sequence | [7,596, 9,172) |
+| 11 | Sort Blocks in Order | [9,172, 9,822) |
+| 12 | Fill And Place Pen Holder | [9,822, 10,572) |
+| 13 | Organize Cosmetics Storage Box | [10,572, 11,582) |
+| 14 | Strike Drum Five Times | [11,582, 12,016) |
+| 15 | Apply Dish Soap Repeatedly | [12,016, 12,575) |
+| 16 | Season Meat Skewers Repeatedly | [12,575, 13,423) |
+| 17 | Pack Items For Shipping | [13,423, 14,163) |
+| 18 | Arrange Flowers and Reed Diffuser | [14,163, 15,307) |
+| 19 | Prepare Breakfast | [15,307, 16,362) |
 
 </details>
 
-Task indices match `meta/tasks.parquet`. Video streams retain their original bytes. Training payload size counts `data/`, `videos/`, `meta/`, and `provenance/`; it excludes this README and `checksums.sha256`. Time steps and duration are counted once per episode.
+Task indices match `meta/tasks.parquet`. Video streams retain their original bytes. Training payload size counts `data/`, `videos/`, and `meta/`; it excludes this README and `checksums.sha256`. Time steps and duration are counted once per episode.
 
 **Publication authorization:** All data were authorized before public release.
 
-**Data revision:** `ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e`. Use this fixed revision with the download examples.
+**Data revision:** Download the current `main` revision; pin its commit hash for reproducible runs.
 
 ## 中文
 
 **20 个任务 × 每任务 1 条 = 20 条轨迹**，共 **16,362 个同步时间步**，全部属于 `train` 划分（`0:20`）。
 
-由 **AgiBot Genie02** 机器人采集的专家示范数据。其余 19 个任务选择其 100 条 Full-Shot 源轨迹中**原始 `episode_id` 最小的一条**；**摆放家庭茶具**选择本次 **27 条茶席轨迹中原始 ID 最小的 `52375079`**。原始 ID、源转换 `episode_index` 与目标 episode 编号分别记录。
+由 **AgiBot Genie02** 机器人采集的专家示范数据，每个任务提供一条示范。
 
-采用 **LeRobot Dataset v3.0**，采样率为 **30 FPS**，总时长 **545.4 秒（约 9.09 分钟）**。训练数据大小为 **215,642,018 字节（约 0.22 GB）**。状态为 `float32[290]`，动作为 `float32[52]`；三路 RGB 相机为头部：640 × 400；左腕、右腕：均为 640 × 528，共 **60 个原编码视频（HEVC）**。
+采用 **LeRobot Dataset v3.0**，采样率为 **30 FPS**，总时长 **545.4 秒（约 9.09 分钟）**。训练数据大小为 **215,613,913 字节（约 0.22 GB）**。状态为 `float32[290]`，动作为 `float32[52]`；三路 RGB 相机为头部：640 × 400；左腕、右腕：均为 640 × 528，共 **60 个原编码视频（HEVC）**。
 
 下载后，将 LeRobot 的 `root` 设置为快照中的 `training-data/real-world/one-shot`。主卡片的[下载与训练示例](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md#download-and-reading)提供状态、动作、任务指令与三路相机观测的 PyTorch batch；value 或 progress 监督由训练方法构造。
 
 <details>
-<summary><strong>原始 ID 与任务—轨迹—行范围映射</strong></summary>
-
-| 任务 / 目标 episode | 任务 / 源任务目录 | 原始 episode_id | 源 episode_index | 行范围 [start, end) |
-| ---: | --- | ---: | ---: | --- |
-| 0 | 用盘子转移积木<br>`task_16249` | 24903119 | 0 | [0, 695) |
-| 1 | 输入键盘数字序列<br>`task_16360` | 24657466 | 1 | [695, 1,301) |
-| 2 | 摆放餐具<br>`task_16924` | 24862628 | 0 | [1,301, 2,249) |
-| 3 | 摆放调料罐<br>`task_18495` | 24904426 | 78 | [2,249, 2,764) |
-| 4 | 将塑料盘装入盘架<br>`task_18678` | 24904665 | 62 | [2,764, 3,324) |
-| 5 | 按按钮五次<br>`task_18806` | 24355082 | 18 | [3,324, 3,827) |
-| 6 | 将小锅放上电磁炉<br>`task_18838` | 24907412 | 99 | [3,827, 4,746) |
-| 7 | 整理桌面工作区<br>`task_18880` | 24862694 | 8 | [4,746, 5,728) |
-| 8 | 整理书本和笔<br>`task_18943` | 24899073 | 0 | [5,728, 6,638) |
-| 9 | 摆放家庭茶具<br>`task_172027` | 52375079 | 5 | [6,638, 7,596) |
-| 10 | 按顺序打开与关闭抽屉<br>`task_19366` | 24659167 | 0 | [7,596, 9,172) |
-| 11 | 按顺序摆放彩色积木<br>`task_20141` | 24903542 | 0 | [9,172, 9,822) |
-| 12 | 装填并摆放笔筒<br>`task_21687` | 29886369 | 41 | [9,822, 10,572) |
-| 13 | 整理化妆品收纳盒<br>`task_23555` | 24903450 | 0 | [10,572, 11,582) |
-| 14 | 敲鼓五次<br>`task_30403` | 24448503 | 41 | [11,582, 12,016) |
-| 15 | 反复按压洗洁精瓶<br>`task_30419` | 24907810 | 99 | [12,016, 12,575) |
-| 16 | 反复给肉串撒料<br>`task_30462` | 26304238 | 99 | [12,575, 13,423) |
-| 17 | 打包待寄送物品<br>`task_31119` | 24903840 | 4 | [13,423, 14,163) |
-| 18 | 插花与摆放藤条香薰<br>`task_31913` | 24905589 | 99 | [14,163, 15,307) |
-| 19 | 准备早餐<br>`task_37438` | 24903652 | 8 | [15,307, 16,362) |
+<summary><strong>任务—轨迹—行范围映射</strong></summary>
+
+| 任务 / episode 编号 | 任务 | 行范围 [start, end) |
+| ---: | --- | --- |
+| 0 | 用盘子转移积木 | [0, 695) |
+| 1 | 输入键盘数字序列 | [695, 1,301) |
+| 2 | 摆放餐具 | [1,301, 2,249) |
+| 3 | 摆放调料罐 | [2,249, 2,764) |
+| 4 | 将塑料盘装入盘架 | [2,764, 3,324) |
+| 5 | 按按钮五次 | [3,324, 3,827) |
+| 6 | 将小锅放上电磁炉 | [3,827, 4,746) |
+| 7 | 整理桌面工作区 | [4,746, 5,728) |
+| 8 | 整理书本和笔 | [5,728, 6,638) |
+| 9 | 摆放家庭茶具 | [6,638, 7,596) |
+| 10 | 按顺序打开与关闭抽屉 | [7,596, 9,172) |
+| 11 | 按顺序摆放彩色积木 | [9,172, 9,822) |
+| 12 | 装填并摆放笔筒 | [9,822, 10,572) |
+| 13 | 整理化妆品收纳盒 | [10,572, 11,582) |
+| 14 | 敲鼓五次 | [11,582, 12,016) |
+| 15 | 反复按压洗洁精瓶 | [12,016, 12,575) |
+| 16 | 反复给肉串撒料 | [12,575, 13,423) |
+| 17 | 打包待寄送物品 | [13,423, 14,163) |
+| 18 | 插花与摆放藤条香薰 | [14,163, 15,307) |
+| 19 | 准备早餐 | [15,307, 16,362) |
 
 </details>
 
-视频保留原始字节。训练数据大小包含 `data/`、`videos/`、`meta/` 和 `provenance/`，不含本 README 与 `checksums.sha256`；时间步与时长按每条轨迹统计一次。
+视频保留原始字节。训练数据大小包含 `data/`、`videos/` 和 `meta/`，不含本 README 与 `checksums.sha256`；时间步与时长按每条轨迹统计一次。
 
-**数据版本：**`ec8c2f2ae736bae8f19ffd9e0ca5cd16dbbe236e`，下载与读取时使用该固定版本。
+**数据版本：**下载当前 `main` 版本；需要复现时固定该版本的提交哈希。
 
 **发布授权：**全部数据均经授权后公开发布。
 

training-data/real-world/one-shot/checksums.sha256

@@ -1,4 +1,4 @@
-3833a7d83f285687f19f0df428322ab69efc4de14229fd41ff59370b302eb5bb  README.md
+a37eace411228e57a91189a6d8c317d8421425d0a8070ac4b0d7393119b69ac6  README.md
 abf892f67d066f54a4428b7639594a51a3c11c207d59b92208eee082f2c0d288  annotations/subtasks.json
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 83542f7c77692a0de24c9254a2114da68394d08ea59be373d78c458e1b6ef259  data/chunk-000/file-001.parquet
@@ -20,16 +20,11 @@ ee36dda4be5b37f6532051e5f5b16f1b66cec84ef41d020b1ecf6f6c51bea80c  data/chunk-000
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 a40d7888df49540b10ea13047fb0fe823d71b95ed70398a4a3adaec6717438a6  data/chunk-000/file-019.parquet
+943412da84277f8d46427884a0f6d149619404110c1588d06f7be74a57c12c84  meta/conversion.json
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 cb686e58fb6c22b6aff18709b64b6e9fc803b7df29343aca910cf817697dee75  meta/info.json
 ca9667639e593294ac2d294c20740d6434678bb6cd0fd87c764eaafe501b8c2f  meta/stats.json
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-f2e92aa3eb2eccb945eb3aa774ace22f17056838120330d6b1ebeac1dbed8807  provenance/conversion.json
-12d5cda0d51272727cb8b6c5a8dc761e223e727a6f8be3e709ebe7920e12d13d  provenance/episode_mapping.json
-c33db864188d6e5f0e327a2a202cbf01ecbbd77c34fb7ade0d93e82f0c538613  provenance/episodes.parquet
-d5ddf2009963ebc38a0a2e4c676ff41e1c10b90c0dc312a30657f30c12ef3f65  provenance/source_episode_ids.csv
-ddb03f9c99d039d7326bc2f827f1a37c28e962691f84587204ce45945189c2d7  provenance/source_episode_ids.md
-73a6f1ed6b0f03a931b8e75099b9f2c0834fd8b77ddc8e073cf770e92f93c83c  provenance/tea-refresh-20261021.json
 faa60b6cb0e015a9de63bcdfb17580126193730a1adc26276d6b35ee70f799ac  videos/observation.images.cam_high/chunk-000/file-000.mp4
 c0709c2425fd72374e170a59fcb63a2a50f081639ccc5f3c2995ba262586e18b  videos/observation.images.cam_high/chunk-000/file-001.mp4
 47e30038bd240129613c66295394419c9d5a91c900e64cf740a6bb25386f7c02  videos/observation.images.cam_high/chunk-000/file-002.mp4

training-data/real-world/one-shot/{provenance → meta}/conversion.json

@@ -1,5 +1,4 @@
 {
-  "selection": "19 tasks retain minimum original IDs from their 100-episode sources; tea uses minimum original ID 52375079 from its approved new 27-episode batch",
   "trimmed_rows": 0,
   "video_processing": "original video bytes retained; no re-encoding",
   "image_statistics": {
@@ -9,19 +8,5 @@
   "format": "LeRobot v3.0",
   "tabular_statistics": "LeRobot 0.4.2 aggregate_stats applied to exact per-episode numerical statistics; per-episode quantiles are count-weighted",
   "public_release_authorized": true,
-  "license": "published with permission",
-  "previous_release_source_manifest_sha256": "a4965649d42fbac1ed1002cfa9b7f4c1d16b84261a2a3dd8b4ad1b53ab418381",
-  "previous_release_selection_sha256": "320e11f207f42cc02acc067584af145b1d1871e09a9cbf680aabd46314da585e",
-  "previous_release_new_complete_video_decodes": 42,
-  "previous_release_reused_complete_video_decodes": 18,
-  "tea_refresh": {
-    "date": "2026-10-21",
-    "source_dataset_id": 224282,
-    "source_task_id": 172027,
-    "replaced_episodes": 1,
-    "new_batch_episodes": 27,
-    "selection": "minimum original ID of the new 27-episode batch",
-    "no_trimming_padding_or_reencoding": true,
-    "source_manifest_sha256": "0817ff23698182080a32978d06c6236cd78dd70b73100dec477e1bcc3cae9a2c"
-  }
+  "license": "published with permission"
 }

training-data/real-world/one-shot/provenance/episode_mapping.json

@@ -1,587 +0,0 @@
-[
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-    "episode_index": 0,
-    "task_index": 0,
-    "source_task": "task_16249",
-    "source_task_id": 16249,
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-      "observation.images.top_head": "task_16249/videos/observation.images.top_head/chunk-000/file-000.mp4"
-    },
-    "camera_aliases": {
-      "observation.images.hand_left": "observation.images.cam_left_wrist",
-      "observation.images.hand_right": "observation.images.cam_right_wrist",
-      "observation.images.top_head": "observation.images.cam_high"
-    },
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-    "dataset_from_index": 0,
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-    },
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-    "trimmed_rows": 0,
-    "task_instruction": "Press the blue button",
-    "selection": "minimum original numeric episode_id among each task's 100 source episodes",
-    "replaced": true
-  },
-  {
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-    "task_index": 6,
-    "source_task": "task_18838",
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-    "source_data": "task_18838/data/chunk-000/file-099.parquet",
-    "source_videos": {
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-      "observation.images.top_head": "task_18838/videos/observation.images.top_head/chunk-000/file-099.mp4"
-    },
-    "camera_aliases": {
-      "observation.images.hand_left": "observation.images.cam_left_wrist",
-      "observation.images.hand_right": "observation.images.cam_right_wrist",
-      "observation.images.top_head": "observation.images.cam_high"
-    },
-    "length": 919,
-    "dataset_from_index": 3827,
-    "dataset_to_index": 4746,
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-    "selection": "minimum original numeric episode_id among each task's 100 source episodes",
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-      "observation.images.hand_right": "task_37438/videos/observation.images.hand_right/chunk-000/file-008.mp4",
-      "observation.images.top_head": "task_37438/videos/observation.images.top_head/chunk-000/file-008.mp4"
-    },
-    "camera_aliases": {
-      "observation.images.hand_left": "observation.images.cam_left_wrist",
-      "observation.images.hand_right": "observation.images.cam_right_wrist",
-      "observation.images.top_head": "observation.images.cam_high"
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-    "dataset_from_index": 15307,
-    "dataset_to_index": 16362,
-    "trimmed_rows": 0,
-    "task_instruction": "Prepare breakfast",
-    "selection": "minimum original numeric episode_id among each task's 100 source episodes",
-    "replaced": true
-  }
-]

training-data/real-world/one-shot/provenance/episodes.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c33db864188d6e5f0e327a2a202cbf01ecbbd77c34fb7ade0d93e82f0c538613
-size 4598

training-data/real-world/one-shot/provenance/source_episode_ids.csv

@@ -1,21 +0,0 @@
-task_index,episode_index,source_dataset_id,source_task_id,source_episode_index,source_episode_id,length,dataset_from_index,dataset_to_index
-0,0,145192,16249,0,24903119,695,0,695
-1,1,145192,16360,1,24657466,606,695,1301
-2,2,145192,16924,0,24862628,948,1301,2249
-3,3,145192,18495,78,24904426,515,2249,2764
-4,4,145192,18678,62,24904665,560,2764,3324
-5,5,145192,18806,18,24355082,503,3324,3827
-6,6,145192,18838,99,24907412,919,3827,4746
-7,7,145192,18880,8,24862694,982,4746,5728
-8,8,145192,18943,0,24899073,910,5728,6638
-9,9,224282,172027,5,52375079,958,6638,7596
-10,10,145192,19366,0,24659167,1576,7596,9172
-11,11,145192,20141,0,24903542,650,9172,9822
-12,12,145192,21687,41,29886369,750,9822,10572
-13,13,145192,23555,0,24903450,1010,10572,11582
-14,14,145192,30403,41,24448503,434,11582,12016
-15,15,145192,30419,99,24907810,559,12016,12575
-16,16,145192,30462,99,26304238,848,12575,13423
-17,17,145192,31119,4,24903840,740,13423,14163
-18,18,145192,31913,99,24905589,1144,14163,15307
-19,19,145192,37438,8,24903652,1055,15307,16362

training-data/real-world/one-shot/provenance/source_episode_ids.md

@@ -1,26 +0,0 @@
-# Original episode IDs
-
-| Task | Episode | Original ID | Source episode | Frames |
-|---:|---:|---:|---:|---:|
-| 0 | 0 | 24903119 | 0 | 695 |
-| 1 | 1 | 24657466 | 1 | 606 |
-| 2 | 2 | 24862628 | 0 | 948 |
-| 3 | 3 | 24904426 | 78 | 515 |
-| 4 | 4 | 24904665 | 62 | 560 |
-| 5 | 5 | 24355082 | 18 | 503 |
-| 6 | 6 | 24907412 | 99 | 919 |
-| 7 | 7 | 24862694 | 8 | 982 |
-| 8 | 8 | 24899073 | 0 | 910 |
-| 9 | 9 | 52375079 | 5 | 958 |
-| 10 | 10 | 24659167 | 0 | 1576 |
-| 11 | 11 | 24903542 | 0 | 650 |
-| 12 | 12 | 29886369 | 41 | 750 |
-| 13 | 13 | 24903450 | 0 | 1010 |
-| 14 | 14 | 24448503 | 41 | 434 |
-| 15 | 15 | 24907810 | 99 | 559 |
-| 16 | 16 | 26304238 | 99 | 848 |
-| 17 | 17 | 24903840 | 4 | 740 |
-| 18 | 18 | 24905589 | 99 | 1144 |
-| 19 | 19 | 24903652 | 8 | 1055 |
-
-Tea uses the minimum original ID of the approved new 27-episode batch (52375079). Other tasks retain their previous minimum-ID selections.

training-data/real-world/one-shot/provenance/tea-refresh-20261021.json

@@ -1,24 +0,0 @@
-{
-  "schema_version": 1,
-  "source_dataset_id": 224282,
-  "source_task_id": 172027,
-  "replacement_rule": "minimum original ID of the new 27-episode batch",
-  "one_shot_selection_exception": true,
-  "replacements": [
-    {
-      "rank": 1,
-      "old_source_dataset_id": 145192,
-      "old_source_task_id": 19328,
-      "old_source_episode_id": 24906950,
-      "old_source_episode_index": 94,
-      "old_length": 1039,
-      "new_source_dataset_id": 224282,
-      "new_source_task_id": 172027,
-      "new_source_episode_id": 52375079,
-      "new_source_episode_index": 5,
-      "new_length": 958,
-      "target_task_index": 9,
-      "target_episode_index": 9
-    }
-  ]
-}

training-data/simulation/full-shot/README.md

@@ -24,7 +24,7 @@ This directory is an independent LeRobot dataset root. Set `root` to `training-d
 <details>
 <summary><strong>Per-task episode allocation</strong></summary>
 
-| Task index | Source task key | Episodes | Episode range / index | Data-row range [start, end) |
+| Task index | Task key | Episodes | Episode range / index | Data-row range [start, end) |
 | ---: | --- | ---: | --- | --- |
 | 0 | `arrange_largest_number` | 100 | [0, 100) | [0, 58332) |
 | 1 | `fill_pen_holder` | 100 | [100, 200) | [58332, 128073) |
@@ -44,7 +44,7 @@ This directory is an independent LeRobot dataset root. Set `root` to `training-d
 
 </details>
 
-Task indices match `meta/tasks.parquet`. Source episode identifiers and task correspondence are recorded in `provenance/`. `checksums.sha256` provides file integrity hashes.
+Task indices match `meta/tasks.parquet`. `checksums.sha256` provides file integrity hashes.
 
 **Publication authorization:** All data were authorized before public release.
 
@@ -58,7 +58,7 @@ Task indices match `meta/tasks.parquet`. Source episode identifiers and task cor
 
 下载后，将 LeRobot 的 `root` 设置为快照中的 `training-data/simulation/full-shot`。主卡片的[下载与训练示例](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md#download-and-reading)提供状态、动作、任务指令与三路相机观测的 PyTorch batch；value 或 progress 监督由训练方法构造。
 
-折叠表列出任务索引、轨迹分配及数据行范围；原始来源标识与对应关系保留在 `provenance/`。
+折叠表列出本子集的任务索引、轨迹分配及数据行范围。
 
 **发布授权：**全部数据均经授权后公开发布。
 

training-data/simulation/full-shot/checksums.sha256

@@ -1,4 +1,4 @@
-8cf7cb03086c27a69df7d2444cadcbbcdbcd195f258682d86d56f5070f5bb663  README.md
+3e85f4566fb993f6a9398a8a993ad44f58e3b7ed48081f808ffcebaad99bd699  README.md
 53405f856ce1b9023265d65f0b82d9cf8806689ccb6dcaa97ea5b268a3818d44  annotations/subtasks.json
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-faa1639571bd1e998b7430ceb0986b0bcc38a02512d414055f32c67f8b05ab51  provenance/conversion.json
-80981b27909b83e858f3d8d778598cb2650f408122feee678a276c63f2ef160e  provenance/episode_mapping.json
-d243793fca5f1eaa457fb404349fba23b2ea695352622cc9bbfb6d53ae78e9ad  provenance/episodes.parquet
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training-data/simulation/full-shot/{provenance → meta}/conversion.json

@@ -1,26 +1,18 @@
 {
   "format": "LeRobot Dataset v3.0",
   "reference_repo": "RoboDojo-Benchmark/RoboDojo",
-  "source_collections": [
-    "1014_supp_data",
-    "1016_supp_data_part1",
-    "1016_supp_data_part2",
-    "RoboDojo"
-  ],
   "video_codecs": [
     "av1",
     "h264"
   ],
   "upstream_reference_revision": "35efbc7dedfdbeeb6e95fb749bd885d73d483e41",
-  "source_manifest_sha256": "8d8aec3f689230e3bbf2528f7279a1a2dfe69ad41c432cbd45022953c9630778",
   "state_dimensions": 14,
   "action_dimensions": 14,
   "fps": 25,
   "video_processing": "byte-identical copies; no re-encoding or fabricated frames",
   "shard_occupancy": "one episode per data file and per camera video; v3.0 path templates",
-  "selection": "100 shortest preselected episodes per task; ties by source episode index",
   "trimmed_rows": 8,
-  "trim_policy": "only the eight explicitly authorized unmatched final rows; full episode count preserved",
+  "trim_policy": "Eight unmatched final data rows were removed; all episodes are retained.",
   "video_intervals": "each camera references [0, retained_length/fps); unused original tail frames preserved",
   "numeric_statistics": "exact global and per-episode statistics; population std; exact quantiles",
   "image_statistics": {
@@ -34,11 +26,62 @@
     "aggregation": "weighted pooled mean and variance"
   },
   "reference_reader": "official lerobot 0.4.2 with pyav 15.1.0; default tolerance 1e-4",
-  "excluded": [
-    "auxiliary",
-    "pickle",
-    "internal trace",
-    "private paths",
-    "original metadata attachments"
+  "trimmed_episodes": [
+    {
+      "episode_index": 75,
+      "task_index": 0,
+      "retained_length": 649,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_high"
+    },
+    {
+      "episode_index": 107,
+      "task_index": 1,
+      "retained_length": 609,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_high"
+    },
+    {
+      "episode_index": 417,
+      "task_index": 4,
+      "retained_length": 319,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_right_wrist"
+    },
+    {
+      "episode_index": 610,
+      "task_index": 6,
+      "retained_length": 441,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_high"
+    },
+    {
+      "episode_index": 727,
+      "task_index": 7,
+      "retained_length": 709,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_right_wrist"
+    },
+    {
+      "episode_index": 971,
+      "task_index": 9,
+      "retained_length": 425,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_right_wrist"
+    },
+    {
+      "episode_index": 1277,
+      "task_index": 12,
+      "retained_length": 587,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_right_wrist"
+    },
+    {
+      "episode_index": 1342,
+      "task_index": 13,
+      "retained_length": 435,
+      "trimmed_last_rows": 1,
+      "deficit_camera": "observation.images.cam_left_wrist"
+    }
   ]
 }

training-data/simulation/full-shot/provenance/episodes.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d243793fca5f1eaa457fb404349fba23b2ea695352622cc9bbfb6d53ae78e9ad
-size 29342

training-data/simulation/one-shot/README.md

@@ -24,7 +24,7 @@ This directory is an independent LeRobot dataset root. Set `root` to `training-d
 <details>
 <summary><strong>Task–episode–row mapping</strong></summary>
 
-| Task index | Source task key | Episodes | Episode range / index | Data-row range [start, end) |
+| Task index | Task key | Episodes | Episode range / index | Data-row range [start, end) |
 | ---: | --- | ---: | --- | --- |
 | 0 | `arrange_largest_number` | 1 | 0 | [0, 579) |
 | 1 | `fill_pen_holder` | 1 | 1 | [579, 1513) |
@@ -44,13 +44,13 @@ This directory is an independent LeRobot dataset root. Set `root` to `training-d
 
 </details>
 
-Task indices match `meta/tasks.parquet`. Source episode identifiers and task correspondence are recorded in `provenance/`. `checksums.sha256` provides file integrity hashes.
+Task indices match `meta/tasks.parquet`. `checksums.sha256` provides file integrity hashes.
 
 **Publication authorization:** All data were authorized before public release.
 
 ## ☰ Subtask annotations
 
-The adaptation supplement retains **58 subtask segments and 8,080 frame labels**. Read `annotations/subtasks.json`, `annotations/subtask_segments.parquet`, and `annotations/frame_labels.parquet` explicitly; they are separate from LeRobot samples and test labels. Segments use `[start_frame, end_frame)`, and frame labels retain the original boundary mask. [Subtask reading](../../../SUBTASKS.md) gives the fixed-revision download and lookup examples.
+The adaptation supplement retains **58 subtask segments and 8,080 frame labels**. Read `annotations/subtasks.json`, `annotations/subtask_segments.parquet`, and `annotations/frame_labels.parquet` explicitly; they are separate from LeRobot samples and test labels. Segments use `[start_frame, end_frame)`, and frame labels retain the original boundary mask. [Subtask reading](../../../SUBTASKS.md) gives download and lookup examples.
 
 ## 中文
 
@@ -62,10 +62,10 @@ The adaptation supplement retains **58 subtask segments and 8,080 frame labels**
 
 下载后，将 LeRobot 的 `root` 设置为快照中的 `training-data/simulation/one-shot`。主卡片的[下载与训练示例](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md#download-and-reading)提供状态、动作、任务指令与三路相机观测的 PyTorch batch；value 或 progress 监督由训练方法构造。
 
-折叠表列出任务索引、轨迹分配及数据行范围；原始来源标识与对应关系保留在 `provenance/`。
+折叠表列出本子集的任务索引、轨迹分配及数据行范围。
 
 **发布授权：**全部数据均经授权后公开发布。
 
 ## ☰ 子任务标注
 
-适配补标保留 **58个子任务区间、8,080条逐帧标签**。显式读取 `annotations/subtasks.json`、`annotations/subtask_segments.parquet` 和 `annotations/frame_labels.parquet`；它们独立于 LeRobot sample 与测试标签。区间采用 `[start_frame, end_frame)`，逐帧表保留原边界标记。固定版本下载与查询示例见[子任务说明](../../../SUBTASKS_zh.md)。
+适配补标保留 **58个子任务区间、8,080条逐帧标签**。显式读取 `annotations/subtasks.json`、`annotations/subtask_segments.parquet` 和 `annotations/frame_labels.parquet`；它们独立于 LeRobot sample 与测试标签。区间采用 `[start_frame, end_frame)`，逐帧表保留原边界标记。下载与查询示例见[子任务说明](../../../SUBTASKS_zh.md)。

training-data/simulation/one-shot/checksums.sha256

@@ -1,4 +1,4 @@
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training-data/simulation/one-shot/{provenance → meta}/conversion.json

@@ -1,7 +1,5 @@
 {
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   "trimmed_rows": 0,
-  "selection": "original episode 000 per task",
   "video_processing": "byte-identical; no re-encoding",
   "image_statistics": {
     "uniform_samples_per_episode": 32,

training-data/simulation/one-shot/provenance/episode_mapping.json

@@ -1,317 +0,0 @@
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-    },
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-      "observation.images.cam_right_wrist": "observation.images.cam_right_wrist"
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