🟦 Simulation One-Shot Training Data / 仿真 One-Shot 训练集
Dataset Card · 简体中文 · Download & Read
15 tasks × 1 demonstration per task = 15 episodes. All episodes belong to the train split (0:15).
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 | 8,080 |
| 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 | 45 · AV1 |
This directory is an independent LeRobot dataset root. Set root to data/simulation/one-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
Task–episode–row mapping
| Task index | Source 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) |
| 2 | fold_clothes |
1 | 2 | [1513, 1814) |
| 3 | hang_mugs |
1 | 3 | [1814, 2327) |
| 4 | insert_tubes |
1 | 4 | [2327, 2647) |
| 5 | make_toast |
1 | 5 | [2647, 3231) |
| 6 | organize_table |
1 | 6 | [3231, 4070) |
| 7 | pack_objects_into_box |
1 | 7 | [4070, 4484) |
| 8 | play_stacking_toy |
1 | 8 | [4484, 5372) |
| 9 | press_by_number |
1 | 9 | [5372, 5750) |
| 10 | put_bottles_into_dustbin |
1 | 10 | [5750, 6113) |
| 11 | stack_blocks |
1 | 11 | [6113, 6381) |
| 12 | store_laptop_and_headphones |
1 | 12 | [6381, 6932) |
| 13 | swap_blocks |
1 | 13 | [6932, 7377) |
| 14 | sweep_blocks |
1 | 14 | [7377, 8080) |
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.
☰ Subtask annotations
Supplementary subtask annotations provide 58 paper-defined segments and 8,080 frame labels, one active subtask per time step. Labels use episode_index and frame_index, with global_index matching the LeRobot index. Initial waiting is part of the first subtask, and the final subtask extends to the episode end. Segments use [start_frame, end_frame); the included last frame is end_frame - 1.
Read these files explicitly using PyArrow or load_subtasks.py; they are separate from the LeRobot samples and benchmark test-set labels. See the directory README for the fixed-revision download example and boundary adjustment mask. dataset_labeler-compatible JSON uses the same subtasks and boundaries as the frame labels.
中文
15 个任务 × 每任务 1 条 = 15 条轨迹，共 8,080 个同步时间步，全部属于 train 划分（0:15）。
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 45 个原编码视频（AV1）。
下载后，将 LeRobot 的 root 设置为快照中的 data/simulation/one-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/。
发布授权：全部数据均经授权后公开发布。
☰ 子任务标注
子任务标注提供 58 个论文子任务区间、8,080 条逐帧标签，每个时间步一个当前子任务。使用 episode_index 与 frame_index 关联，并核对 global_index 与 LeRobot 的 index 一致。起始等待归入第一个子任务，最后一个子任务持续至轨迹末帧；区间为 [start_frame, end_frame)，包含式末帧为 end_frame - 1。
通过 PyArrow 或 load_subtasks.py 显式读取。标注独立于 LeRobot sample，与评测集标签分开；目录 README 提供固定版本的下载示例及边界调整标记说明。 另提供 dataset_labeler 兼容 JSON，与逐帧标签使用相同的子任务和边界。