Joocjun/GR1-Tabletop-Merged-300x24

---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- GR00T
- GR1
- robocasa
- simulation
- manipulation
size_categories:
- 10K<n<100K
---

# GR1 Tabletop Merged LeRobot Datasets

Merged and subsampled versions of the GR1 tabletop manipulation datasets from the [NVIDIA PhysicalAI-Robotics-GR00T-X-Embodiment-Sim](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim) collection, formatted in **LeRobot v2.0** format.

## Dataset Variants

| Variant | Demos/Task | Tasks | Total Episodes | Total Frames | Approx Size |
|---------|-----------|-------|----------------|--------------|-------------|
| `1000x24/` | 1000 | 24 folders, 186 unique tasks | 24,000 | 6,020,058 | ~40 GB |
| `300x24/` | 300 | 24 folders, 186 unique tasks | 7,200 | 1,803,236 | ~12 GB |
| `100x24/` | 100 | 24 folders, 185 unique tasks | 2,400 | 602,846 | ~4 GB |

## Source Dataset

The original per-task datasets were downloaded from:
**[nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim)**

To download the original dataset:
```bash
# Install huggingface_hub
pip install huggingface_hub

# Download specific task datasets
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim",
repo_type="dataset",
allow_patterns="gr1_unified.PnP*", # or specific task patterns
local_dir="./original_data"
)
```

The 24 source task folders used (each with 1000 demos):
- 6 **PnP** tasks: Bottle→Cabinet, Can→Drawer, Cup→Drawer, Milk→Microwave, Potato→Microwave, Wine→Cabinet
- 18 **PosttrainPnPNovel** tasks: Various object transfers between surfaces (Cuttingboard→{Basket,Cardboardbox,Pan,Pot,Tieredbasket}, Placemat→{Basket,Bowl,Plate,Tieredshelf}, Plate→{Bowl,Cardboardbox,Pan,Plate}, Tray→{Cardboardbox,Plate,Pot,Tieredbasket,Tieredshelf})

## Processing

Each merged dataset was created by:

1. **Subsampling**: Randomly sampling N episodes (with seed=42) from each of the 24 task folders
2. **Merging**: Combining all sampled episodes into a single dataset with:
- Re-indexed episode IDs (0 to total-1)
- Globally re-indexed task IDs across all source folders
- Updated parquet files with corrected `episode_index`, `task_index`, and `index` columns
- Consolidated `meta/` files (`info.json`, `episodes.jsonl`, `tasks.jsonl`, `modality.json`)

### Note on task counts

The `100x24` variant has 185 unique tasks instead of 186. This is because some task folders contain multiple task variants (e.g., the `PosttrainPnPNovelFromTrayToTieredbasket` folder has 10 different object variants like eggplant, tomato, etc.). The "eggplant" variant only appears in 58 out of 1000 source episodes, so when randomly sampling only 100 episodes, none of the eggplant episodes were selected. This is expected behavior with random subsampling.

## Dataset Structure (LeRobot v2.0)

```
<variant>/
├── data/
│ └── chunk-000/
│ ├── episode_000000.parquet
│ ├── episode_000001.parquet
│ └── ...
├── videos/
│ └── chunk-000/
│ └── observation.images.ego_view/
│ ├── episode_000000.mp4
│ ├── episode_000001.mp4
│ └── ...
└── meta/
├── info.json
├── episodes.jsonl
├── tasks.jsonl
├── modality.json
├── stats.json
└── relative_stats.json
```

### Features

| Feature | Type | Shape |
|---------|------|-------|
| `observation.images.ego_view` | video (h264) | [256, 256, 3] @ 20fps |
| `observation.state` | float64 | [44] |
| `action` | float64 | [44] |
| `timestamp` | float64 | [1] |
| `next.reward` | float64 | [1] |
| `next.done` | bool | [1] |
| `task_index` | int64 | [1] |
| `episode_index` | int64 | [1] |
| `annotation.human.coarse_action` | int64 | [1] |

Robot: **GR1ArmsAndWaistFourierHands** (44-DOF: arms, hands, legs, neck, waist)

## Usage

```python
# Download a specific variant
from huggingface_hub import snapshot_download

snapshot_download(
repo_id="Joocjun/GR1-Tabletop-Merged-LeRobot",
repo_type="dataset",
allow_patterns="100x24/**", # or "300x24/**" or "1000x24/**"
local_dir="./gr1_merged"
)
```

## License

Apache 2.0 (following the original NVIDIA dataset license)

许可证: Apache-2.0
任务类别:
- 机器人学
标签:
- LeRobot
- GR00T
- GR1
- robocasa
- 仿真
- 机器人操作
样本规模分类:
- 10K<n<100K

# GR1 桌面合并 LeRobot 数据集

本数据集为来自[NVIDIA PhysicalAI-Robotics-GR00T-X-Embodiment-Sim](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim) 合集的GR1桌面操作数据集的合并与下采样版本，采用**LeRobot v2.0**格式进行组织。

## 数据集变体

| 变体路径 | 演示任务数/总任务数 | 任务详情 | 总片段数 | 总帧数 | 近似体积 |
|---------|-----------|-------|----------------|--------------|-------------|
| `1000x24/` | 1000 | 24个文件夹，186个唯一任务 | 24,000 | 6,020,058 | ~40 GB |
| `300x24/` | 300 | 24个文件夹，186个唯一任务 | 7,200 | 1,803,236 | ~12 GB |
| `100x24/` | 100 | 24个文件夹，185个唯一任务 | 2,400 | 602,846 | ~4 GB |

## 源数据集

原始单任务数据集下载自：**[nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim](https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim)**

若需下载原始数据集：
bash
# 安装 huggingface_hub 库
pip install huggingface_hub

# 下载指定任务数据集
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim",
repo_type="dataset",
allow_patterns="gr1_unified.PnP*", # 或指定任务匹配模式
local_dir="./original_data"
)

本次使用的24个源任务文件夹（每个文件夹含1000个演示片段）：
- 6个**拾取放置（Pick-and-Place，简称PnP）**任务：瓶子→橱柜、罐子→抽屉、杯子→抽屉、牛奶盒→微波炉、土豆→微波炉、酒瓶→橱柜
- 18个**PosttrainPnPNovel**任务：各类物体在不同台面间的转移（切菜板→{篮筐、纸箱、平底锅、锅、分层篮}，餐垫→{篮筐、碗、盘子、分层架}，盘子→{碗、纸箱、平底锅、盘子}，托盘→{纸箱、盘子、锅、分层篮、分层架}）

## 数据处理流程

每个合并数据集的生成流程如下：
1. **下采样**：从24个任务文件夹中各随机采样N个片段（随机种子设为42）
2. **合并**：将所有采样得到的片段整合为单个数据集，包含：
- 重新索引的片段ID（范围0至总片段数减1）
- 跨所有源文件夹的全局统一任务索引
- 更新后的parquet文件，修正了`episode_index`、`task_index`和`index`字段
- 整合后的`meta/`目录文件（`info.json`、`episodes.jsonl`、`tasks.jsonl`、`modality.json`）

### 任务数量说明
`100x24`变体的唯一任务数为185而非186，原因是部分任务文件夹包含多个任务变体（例如`PosttrainPnPNovelFromTrayToTieredbasket`文件夹包含10种不同的物体变体，如茄子、番茄等）。其中“茄子”变体仅在1000个源片段中出现58次，因此当随机采样仅100个片段时，未选中任何茄子变体的片段，该现象为随机下采样的正常结果。

## 数据集结构（LeRobot v2.0格式）

<variant>/
├── data/
│ └── chunk-000/
│ ├── episode_000000.parquet
│ ├── episode_000001.parquet
│ └── ...
├── videos/
│ └── chunk-000/
│ └── observation.images.ego_view/
│ ├── episode_000000.mp4
│ ├── episode_000001.mp4
│ └── ...
└── meta/
├── info.json
├── episodes.jsonl
├── tasks.jsonl
├── modality.json
├── stats.json
└── relative_stats.json

### 数据特征
| 特征名称 | 数据类型 | 参数/维度 |
|---------|------|-------|
| `observation.images.ego_view` | 视频（h264编码） | 分辨率[256, 256, 3]，帧率20fps |
| `observation.state` | float64 | 维度[44] |
| `action` | float64 | 维度[44] |
| `timestamp` | float64 | 维度[1] |
| `next.reward` | float64 | 维度[1] |
| `next.done` | 布尔值 | 维度[1] |
| `task_index` | int64 | 维度[1] |
| `episode_index` | int64 | 维度[1] |
| `annotation.human.coarse_action` | int64 | 维度[1] |

机器人型号：**GR1ArmsAndWaistFourierHands**（44自由度：双臂、双手、腿部、颈部与腰部）

## 使用方法
python
# 下载指定变体
from huggingface_hub import snapshot_download

snapshot_download(
repo_id="Joocjun/GR1-Tabletop-Merged-LeRobot",
repo_type="dataset",
allow_patterns="100x24/**", # 或 "300x24/**" 或 "1000x24/**"
local_dir="./gr1_merged"
)

## 许可协议
采用Apache 2.0协议（遵循原始NVIDIA数据集的许可协议）