Datasets:

wzlllong
/

value-model-benchmark-training-data

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-# 🟧 Real-World Full-Shot Training Data / 真机 Full-Shot 训练集
-[Dataset Card](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data#real-world) · [简体中文](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md#real-world) · [Download & Read](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data#download-and-reading)
-**20 tasks × 100 demonstrations per task = 2,000 episodes.** All episodes belong to the `train` split (`0:2000`).
-Expert demonstrations collected with the AgiBot Genie02 robot.
-| Content | Specification |
-| --- | --- |
-| Platform | AgiBot Genie02 |
-| Dataset format | LeRobot Dataset v3.0 |
-| 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 |
-| Robot state | `observation.state`: `float32[290]` |
-| Robot action | `action`: `float32[52]` |
-| RGB cameras | Head: 640 × 400; left/right wrist: 640 × 528 each |
-| Camera videos | 6,000 · HEVC |
-This directory is an independent LeRobot dataset root. Set `root` to `data/real-world/full-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/data/real-world/full-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>Per-task episode allocation</strong></summary>
-| Task index | Display task | Episodes | Episode range / index | Data-row range [start, end) |
-| ---: | --- | ---: | --- | --- |
-| 0 | Transfer Blocks Using Plate | 100 | [0, 100) | [0, 57018) |
-| 1 | Enter Keypad Number Sequence | 100 | [100, 200) | [57018, 116432) |
-| 2 | Set the Table | 100 | [200, 300) | [116432, 216958) |
-| 3 | Arrange Seasoning Jars | 100 | [300, 400) | [216958, 273619) |
-| 4 | Load Plastic Plates Into Rack | 100 | [400, 500) | [273619, 335961) |
-| 5 | Press Button Five Times | 100 | [500, 600) | [335961, 385985) |
-| 6 | Place Pot On Induction Cooker | 100 | [600, 700) | [385985, 455845) |
-| 7 | Organize Desktop Workstation | 100 | [700, 800) | [455845, 543757) |
-| 8 | Organize Books And Pen | 100 | [800, 900) | [543757, 627278) |
-| 9 | Arrange Family Tea Set | 100 | [900, 1000) | [627278, 719790) |
-| 10 | Operate Drawers In Sequence | 100 | [1000, 1100) | [719790, 887196) |
-| 11 | Sort Blocks in Order | 100 | [1100, 1200) | [887196, 936710) |
-| 12 | Fill And Place Pen Holder | 100 | [1200, 1300) | [936710, 1012829) |
-| 13 | Organize Cosmetics Storage Box | 100 | [1300, 1400) | [1012829, 1100220) |
-| 14 | Strike Drum Five Times | 100 | [1400, 1500) | [1100220, 1141610) |
-| 15 | Apply Dish Soap Repeatedly | 100 | [1500, 1600) | [1141610, 1197495) |
-| 16 | Season Meat Skewers Repeatedly | 100 | [1600, 1700) | [1197495, 1276003) |
-| 17 | Pack Items For Shipping | 100 | [1700, 1800) | [1276003, 1347546) |
-| 18 | Arrange Flowers and Reed Diffuser | 100 | [1800, 1900) | [1347546, 1445636) |
-| 19 | Prepare Breakfast | 100 | [1900, 2000) | [1445636, 1534321) |
-</details>
-Task indices match `meta/tasks.parquet`. Source episode identifiers and task correspondence are recorded in `provenance/`. `checksums.sha256` provides file integrity hashes.
-**Publication authorization:** All data were authorized before public release.
-**Data revision:** `3bcf64ae6d2a39e0795f413b65bfd08a3a8c6f95`. Use this fixed revision with the download examples.
-## 中文
-**20 个任务 × 每任务 100 条 = 2,000 条轨迹**，共 **1,534,321 个同步时间步**，全部属于 `train` 划分（`0:2000`）。
-由 AgiBot Genie02 机器人采集的专家示范数据。
-平台为 **AgiBot Genie02**，采用 **LeRobot Dataset v3.0**，采样率为 **30 FPS**。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 **6,000 个原编码视频（HEVC）**。
-下载后，将 LeRobot 的 `root` 设置为快照中的 `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/`。
-**数据版本：**`3bcf64ae6d2a39e0795f413b65bfd08a3a8c6f95`，下载与读取时使用该固定版本。
-**发布授权：**全部数据均经授权后公开发布。

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