🟦 Simulation Full-Shot Training Data / 仿真 Full-Shot 训练集
Dataset Card · 简体中文 · Download & Read
15 tasks × 100 demonstrations per task = 1,500 episodes. All episodes belong to the train split (0:1500).
Based on open-source RoboDojo data reviewed by our team, with selected demonstrations recorded again.
| Content | Specification |
|---|---|
| Platform | ARX |
| Dataset format | LeRobot Dataset v3.0 |
| Synchronized time steps | 789,612 |
| Sampling rate | 25 FPS |
| Robot state | observation.state: float32[14] |
| Robot action | action: float32[14] |
| RGB cameras | High, left wrist, right wrist: 640 × 480 each |
| Camera videos | 4,500 · AV1 / H.264 |
This directory is an independent LeRobot dataset root. Set root to training-data/simulation/full-shot inside the downloaded snapshot. The download and training examples 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 · Shared trajectory contents and task descriptions
Per-task episode allocation
| 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) |
| 2 | fold_clothes |
100 | [200, 300) | [128073, 158424) |
| 3 | hang_mugs |
100 | [300, 400) | [158424, 208157) |
| 4 | insert_tubes |
100 | [400, 500) | [208157, 240395) |
| 5 | make_toast |
100 | [500, 600) | [240395, 299974) |
| 6 | organize_table |
100 | [600, 700) | [299974, 355130) |
| 7 | pack_objects_into_box |
100 | [700, 800) | [355130, 436549) |
| 8 | play_stacking_toy |
100 | [800, 900) | [436549, 529154) |
| 9 | press_by_number |
100 | [900, 1000) | [529154, 568074) |
| 10 | put_bottles_into_dustbin |
100 | [1000, 1100) | [568074, 609854) |
| 11 | stack_blocks |
100 | [1100, 1200) | [609854, 636404) |
| 12 | store_laptop_and_headphones |
100 | [1200, 1300) | [636404, 688851) |
| 13 | swap_blocks |
100 | [1300, 1400) | [688851, 732459) |
| 14 | sweep_blocks |
100 | [1400, 1500) | [732459, 789612) |
Task indices match meta/tasks.parquet. checksums.sha256 provides file integrity hashes.
Publication authorization: All data were authorized before public release.
中文
15 个任务 × 每任务 100 条 = 1,500 条轨迹，共 789,612 个同步时间步，全部属于 train 划分（0:1500）。
基于 RoboDojo 开源数据整理，经团队审核后，对部分示例重新采集。
平台为 ARX，采用 LeRobot Dataset v3.0，采样率为 25 FPS。状态为 float32[14]，动作为 float32[14]；三路 RGB 相机为高位、左腕、右腕：均为 640 × 480，共 4,500 个原编码视频（AV1 / H.264）。
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。
Subtask definitions / 子任务定义
annotations/subtasks.json provides S-numbered reference definitions and source citations; this subset has no temporal subtask labels. See Subtask reading. / annotations/subtasks.json 提供S编号参考定义与来源引用，本子集没有子任务时间标注；见子任务说明。