# ☰ Simulation One-Shot Subtask Annotations / 仿真 One-Shot 子任务标注

**15 tasks · 15 episodes · 58 subtask segments · 8,080 frame labels · 25 FPS**

These supplementary annotations label the active semantic subtask in the simulation One-Shot **training demonstrations**. The English S1…Sn definitions follow [RoboValue, Appendix C.2](https://arxiv.org/abs/2610.26663); Chinese text is a translation. Each `(task_key, subtask_id)` pair has a unique `semantic_id`.

## Label conventions / 标注约定

| Item / 项目 | Convention / 约定 |
| --- | --- |
| Frame identity / 帧标识 | Zero-based `episode_index` and `frame_index`; `global_index` matches the LeRobot data row `index` / 从 0 开始，按整数索引关联 |
| Segments / 区间 | `[start_frame, end_frame)` includes the start and excludes the end; `inclusive_end_frame = end_frame - 1` / 起始帧包含，末端不包含，包含式末帧等于结束值减 1 |
| Time / 时间 | `timestamp_seconds = frame_index / 25`; one frame is **0.04 seconds** / 每帧 0.04 秒 |
| Coverage / 覆盖 | One paper subtask per frame; initial waiting belongs to S1, and the final subtask extends to the last episode frame / 每帧一个论文子任务，起始等待并入 S1，最后一个子任务持续至轨迹末帧 |
| Boundary bands / 边界调整带 | Typically ±13 frames (0.52 s); Organize Table S4 (keyboard) retains ±25 frames (1 s). `boundary_uncertain` marks these adjustment bands / 通常 ±13 帧，整理桌面的 S4 键盘步骤保留 ±25 帧，由该布尔字段标记 |

Only paper subtasks are used; there are no idle, completed, or incomplete-task label classes. Boundary bands describe semantic annotation adjustment, independently of LeRobot's default video-loading tolerance of **0.0001 seconds**. They do not remove frames or change the labels.

The accepted boundaries include frame **210** in Store Laptop And Headphones S2, frame **213** in Sweep Blocks S1, and frame **380** in Sweep Blocks S2. Press By Number S1 starts at frame **0** and follows the paper's printed S1–S4 definitions.

这些标签用于仿真 One-Shot 训练示例，与论文评测集标签分开。仅使用论文子任务，没有空闲、任务已完成或任务未完成类别；边界调整带不改变默认视频加载容差，不删除帧。收纳笔记本电脑与耳机的 S2 包含第 **210 帧**；清扫积木的 S1 包含第 **213 帧**，S2 包含第 **380 帧**；按数字按钮的 S1 从第 **0 帧**开始。

## Files / 文件

| File / 文件 | Contents / 内容 |
| --- | --- |
| [taxonomy.json](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/taxonomy.json) | Paper subtask definitions and task IDs / 论文子任务定义与任务编号 |
| [segments.json](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/segments.json), [segments.parquet](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/segments.parquet) | 58 labeled intervals / 58 个标注区间 |
| [frame_labels.parquet](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/frame_labels.parquet) | 8,080 labels with integer join keys, text, and boundary mask / 逐帧标签、整数关联键、文本与边界标记 |
| [metadata.json](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/metadata.json) | Dataset identity and annotation conventions / 数据身份与标注约定 |
| [validation.json](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/validation.json) | Coverage, consistency, and accepted-boundary checks / 覆盖、一致性与已确认边界检查 |
| [load_subtasks.py](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/load_subtasks.py) | Explicit loading, SHA256 validation, and sample lookup / 显式读取、SHA256 校验与帧查询 |
| [dataset_labeler/](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/dataset_labeler) | 15 task-level SIA+CSPC JSON files and a Transfer all boundary export / 15 个任务级兼容 JSON 与 Transfer all 边界点导出 |
| [manifest.json](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/manifest.json) | SHA256 and byte size of the 23 annotation payload files; the manifest is not self-hashed / 23 个标注文件的校验值与字节数，清单不校验自身 |

## Download and read / 下载与读取

This example downloads **only the annotation directory** at a fixed revision. It requires `huggingface_hub` and `pyarrow` (also installed with LeRobot).

以下示例只下载固定版本的标注目录，不下载视频。需要 `huggingface_hub` 与 `pyarrow`，LeRobot 环境已包含这两个依赖。

```python
import importlib.util
import os
from pathlib import Path

from huggingface_hub import snapshot_download

repo_id = "wzlllong/value-model-benchmark-training-data"
prefix = "data/simulation/one-shot"
revision = "7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145"
local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
snapshot = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    revision=revision,
    allow_patterns=[f"{prefix}/annotations/subtasks/**"],
    local_dir=local_dir,
)
root = Path(snapshot) / prefix
spec = importlib.util.spec_from_file_location(
    "robovalue_subtasks", root / "annotations/subtasks/load_subtasks.py"
)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
annotations = module.load_subtasks(root)
label = annotations.label(episode_index=0, frame_index=0, global_index=0)
print(annotations.table.num_rows, label["semantic_id"], label["subtask_text_en"])
```

With the matching LeRobot simulation One-Shot dataset loaded as `dataset`, use `label = annotations.for_sample(dataset[0])`. The helper checks the episode/frame keys and verifies `global_index` against the sample's `index`. For PyArrow, join on `episode_index` and `frame_index` and make the same index check. Attach labels before PyTorch batch collation when your training method needs them; LeRobot does not automatically include annotation files in samples.

读取同一版本的仿真 One-Shot LeRobot 数据后，使用 `label = annotations.for_sample(dataset[0])` 关联标签；该方法检查轨迹与帧编号，并核对全局数据行索引。训练需要子任务标签时，在 PyTorch batch 合并前显式关联。

## dataset_labeler-compatible JSON / 兼容标注格式

The [15 task-level JSON files](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/dataset_labeler) retain the `dataset_labeler` SIA+CSPC record format. Each `<task_key>_sia_cspc_annotations.json` maps a **dataset-root-relative head-camera video path** to one annotation record. `task_key` is the canonical simulation task name, including `sweep_blocks`. Resolve a video key against the local simulation One-Shot dataset root when relocating the files.

[15 个任务级 JSON 文件](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/dataset_labeler)保持 `dataset_labeler` 的 SIA+CSPC 记录格式；每个 `<task_key>_sia_cspc_annotations.json` 以**相对仿真 One-Shot 数据根目录的头部相机视频路径**为键，对应一条轨迹记录。任务名使用发布数据的统一名称，包括 `sweep_blocks`；移动数据后，由读取者将相对路径与本地数据根目录拼接。

| Field / 字段 | Meaning / 含义 |
| --- | --- |
| `episode` | The same dataset-root-relative video path as the object key / 与外层键相同的数据根目录相对视频路径 |
| `marks` | Ordered `{"type": "s", "frame": integer, "time": seconds}` boundary points / 按顺序排列的子任务边界点 |
| `duration` | Episode duration in seconds / 轨迹时长，单位秒 |
| `task` | Canonical simulation task name / 统一仿真任务名 |
| `metric`, `group` | `SIA+CSPC`, `one-shot` |
| `episode_name` | Published target episode name, e.g. `episode_000000` / 发布的目标轨迹名称 |
| `gap` | Preserved labeler-format parameter: `20` / 保留的标注工具格式参数，值为 20 |

`marks` contains frame **0**, every segment's exclusive `end_frame`, and the episode-length terminal point. For **N** subtasks there are **N + 1** points; consecutive points recover `[start_frame, end_frame)` intervals in S1…Sn order. The terminal point closes the last interval and does not create a completed-task label. `time = frame / 25`. The accepted boundaries are unchanged: Store Laptop And Headphones uses **[0, 111, 211, 551]** and Sweep Blocks uses **[0, 214, 381, 703]**. Their inclusive last frames remain **210**, **213**, and **380**.

`marks` 包含第 **0 帧**、各区间的半开结束点，以及等于轨迹帧数的末端点。**N** 个子任务对应 **N + 1** 个边界点，相邻点按 S1…Sn 顺序还原 `[start_frame, end_frame)` 区间；末端点仅闭合最后一个子任务，不增加“任务已完成”标签。`time = frame / 25`。已确认的标签和边界均保持不变。

[`exceptional_intervals.json`](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/dataset_labeler/exceptional_intervals.json) follows the labeler's **Transfer all** output: a list of **15** `{"episode": video_path, "frames": boundary_points}` records containing **73 boundary points**. Those points close the same **58 intervals**; they are not additional subtask labels. The JSON export, `segments.json`, and `frame_labels.parquet` describe the same **58 segments / 8,080 labels**. This is a portable format export for these training demonstrations; the integer-keyed Parquet files and helper remain available for explicit training-label joins.

[`exceptional_intervals.json`](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/7fd21cb2519b4d59cbe9fe8b23cfdf477e3ad145/data/simulation/one-shot/annotations/subtasks/dataset_labeler/exceptional_intervals.json)保持标注工具 **Transfer all** 的列表格式，每条记录为 `{"episode": 视频路径, "frames": 边界点列表}`。共 **15 条轨迹、73 个边界点**，对应相同的 **58 个子任务区间、8,080 条逐帧标签**；边界点不增加标签类别。用于训练时仍可通过整数索引和原读取工具显式关联标签。

After the annotation-only download example above, read the compatibility JSON locally without downloading videos:

运行上方仅下载标注的示例后，可在本地读取兼容 JSON；以下代码不下载或解码视频。

```python
import json

labeler_dir = root / "annotations/subtasks/dataset_labeler"
task_file = sorted(labeler_dir.glob("*_sia_cspc_annotations.json"))[0]
by_video = json.loads(task_file.read_text(encoding="utf-8"))
video_path, record = next(iter(by_video.items()))
points = record["marks"]
intervals = [
    {"subtask_id": f"S{i + 1}", "start_frame": left["frame"],
     "end_frame": right["frame"]}
    for i, (left, right) in enumerate(zip(points, points[1:]))
]
assert intervals[0]["start_frame"] == 0
assert all(part["start_frame"] < part["end_frame"] for part in intervals)
print(record["episode_name"], video_path, intervals)
# Video location, when the training payload is present: root / video_path
```