Annotation_with_action_lerobotv21/README.md

# RoboInter-Data: LeRobot v2.1 Format (Actions + Annotations + Videos)

The primary data format of [RoboInter-Data](https://huggingface.co/datasets/InternRobotics/RoboInter-Data). Contains robot **actions**, camera **observations**, and rich **intermediate representation annotations** in [LeRobot v2.1](https://github.com/huggingface/lerobot) format (parquet + MP4 videos), ready for policy training. Especially, we calculate the delta EEF (gripper) action of Droid (instead of the joint velocity or the origin cartesian action of the base).

| Sub-dataset | Source | Robot | Episodes | Frames | Tasks | Image Size | Raw Image Size |
|-------------|--------|-------|----------|--------|-------|------------|-------|
| `lerobot_droid_anno` | [DROID](https://droid-dataset.github.io/) | Franka + Robotiq | 152,986 | 46,259,014 | 43,026 | 320 x 180 | 640 x 360 |
| `lerobot_rh20t_anno` | [RH20T](https://rh20t.github.io/) | Multiple | 82,894 | 40,755,632 | 146 | 320 x 180 | 640 x 360 |

Both datasets share `fps=10`, `chunks_size=1000`, and the same annotation schema.

## Directory Layout

```
lerobot_droid_anno/  (or lerobot_rh20t_anno/)
├── meta/
│   ├── info.json              # Dataset metadata (fps, features, shapes, etc.)
│   ├── episodes.jsonl         # Per-episode info (one JSON per line)
│   ├── episodes_stats.jsonl   # Per-episode statistics
│   └── tasks.jsonl            # Task/instruction mapping
├── data/
│   └── chunk-{NNN}/           # Parquet data chunks (1,000 episodes per chunk)
│       ├── episode_000000.parquet
│       ├── episode_000001.parquet
│       └── ...
└── videos/
    └── chunk-{NNN}/
        ├── observation.images.primary/
        │   └── episode_{NNNNNN}.mp4
        └── observation.images.wrist/
            └── episode_{NNNNNN}.mp4
```

---

## Data Fields

### Core Fields (Shared by DROID & RH20T)

| Field | Shape | Type | Description |
|-------|-------|------|-------------|
| `action` | (7,) | float64 | Delta EEF action: [delta_x, delta_y, delta_z, delta_rx, delta_ry, delta_rz, gripper_command] |
| `state` | (7,) | float64 | EEF state: [x, y, z, rx, ry, rz, gripper_state] |
| `observation.images.primary` | (180, 320, 3) | video (H.264) | Primary camera RGB video |
| `observation.images.wrist` | (180, 320, 3) | video (H.264) | Wrist camera RGB video |

### Metadata Fields (Shared)

| Field | Type | Description |
|-------|------|-------------|
| `episode_name` | string | Episode unique identifier, e.g. `"3072_exterior_image_1_left"` |
| `camera_view` | string | Camera perspective, e.g. `"exterior_image_1_left"` |
| `task` | string | Task language description (via `task_index` -> `tasks.jsonl`) |
| `episode_index` | int64 | Episode index in dataset |
| `frame_index` | int64 | Frame index within episode |
| `timestamp` | float32 | Timestamp in seconds (`frame_index / fps`) |
| `index` | int64 | Global frame index across all episodes |
| `task_index` | int64 | Index into `tasks.jsonl` |

---

### Other Information Fields — DROID Only

`lerobot_droid_anno` contains the following additional fields from the original DROID dataset:

| Field | Shape | Type | Description |
|-------|-------|------|-------------|
| `other_information.language_instruction_2` | (1,) | string | Alternative language instruction (source 2) |
| `other_information.language_instruction_3` | (1,) | string | Alternative language instruction (source 3) |
| `other_information.action_delta_tcp_pose` | (7,) | float64 | Delta TCP pose action: [dx, dy, dz, drx, dry, drz, gripper] |
| `other_information.action_delta_wrist_pose` | (7,) | float64 | Delta wrist pose action: [dx, dy, dz, drx, dry, drz, gripper] |
| `other_information.action_tcp_pose` | (7,) | float64 | Absolute TCP pose: [x, y, z, rx, ry, rz, gripper] |
| `other_information.action_wrist_pose` | (7,) | float64 | Absolute wrist pose: [x, y, z, rx, ry, rz, gripper] |
| `other_information.action_gripper_velocity` | (1,) | float64 | Gripper velocity |
| `other_information.action_joint_position` | (7,) | float64 | Joint position action: [j1..j7] |
| `other_information.action_joint_velocity` | (7,) | float64 | Joint velocity action: [j1..j7] |
| `other_information.action_cartesian_velocity` | (6,) | float64 | Cartesian velocity: [vx, vy, vz, wx, wy, wz] |
| `other_information.observation_joint_position` | (7,) | float64 | Observed joint positions: [j1..j7] |
| `other_information.observation_gripper_position` | (1,) | float64 | Observed gripper position |
| `other_information.observation_gripper_open_state` | (1,) | float64 | Gripper open state |
| `other_information.observation_gripper_pose6d` | (6,) | float64 | Gripper 6D pose: [x, y, z, rx, ry, rz] |
| `other_information.observation_tcp_pose6d` | (6,) | float64 | TCP 6D pose: [x, y, z, rx, ry, rz] |
| `other_information.is_first` | (1,) | bool | First frame flag |
| `other_information.is_last` | (1,) | bool | Last frame flag |
| `other_information.is_terminal` | (1,) | bool | Terminal state flag |

### Other Information Fields — RH20T Only

`lerobot_rh20t_anno` contains the following additional fields from the original RH20T dataset:

| Field | Shape | Type | Description |
|-------|-------|------|-------------|
| `other_information.action_delta_tcp_pose` | (7,) | float64 | Delta TCP pose action: [dx, dy, dz, drx, dry, drz, gripper] |
| `other_information.action_tcp_pose` | (7,) | float64 | Absolute TCP pose: [x, y, z, rx, ry, rz, gripper] |
| `other_information.gripper_command` | (1,) | float64 | Gripper command |
| `other_information.observation_joint_position` | (14,) | float64 | Observed joint positions: [j1..j14] |
| `other_information.observation_gripper_open_state` | (1,) | float64 | Gripper open state |
| `other_information.observation_gripper_pose6d` | (6,) | float64 | Gripper 6D pose: [x, y, z, rx, ry, rz] |
| `other_information.tcp_camera` | (7,) | float64 | TCP in camera frame: [x, y, z, qx, qy, qz, qw] |
| `other_information.tcp_base` | (7,) | float64 | TCP in base frame: [x, y, z, qx, qy, qz, qw] |
| `other_information.gripper` | (1,) | string | Gripper metadata (JSON) |
| `other_information.is_first` | (1,) | bool | First frame flag |
| `other_information.is_last` | (1,) | bool | Last frame flag |
| `other_information.is_terminal` | (1,) | bool | Terminal state flag |

> **Key difference:** DROID has 7-DoF joint positions and richer action representations (wrist pose, joint/cartesian velocities). RH20T has 14-DoF joint positions, TCP transforms in camera/base frames, and gripper metadata JSON.

---

### Annotation Fields (Shared by DROID & RH20T)

All annotation fields are prefixed with `annotation.` and stored as JSON strings. Empty string `""` means no annotation is available for that frame.

| Field | Format | Description |
|-------|--------|-------------|
| `annotation.time_clip` | `[[start, end], ...]` | Subtask temporal segments (frame ranges) |
| `annotation.instruction_add` | string | Structured task language instruction |
| `annotation.substask` | string | Current subtask description |
| `annotation.primitive_skill` | string | Primitive skill label (pick, place, push, twist, etc.) |
| `annotation.segmentation` | string | Segmentation reference (path) |
| `annotation.object_box` | `[[x1, y1], [x2, y2]]` | Manipulated object bounding box |
| `annotation.placement_proposal` | `[[x1, y1], [x2, y2]]` | Target placement bounding box |
| `annotation.trace` | `[[x, y], ...]` | Future 10-frame gripper trajectory waypoints |
| `annotation.gripper_box` | `[[x1, y1], [x2, y2]]` | Gripper bounding box |
| `annotation.contact_frame` | int / -1 | Frame index when gripper contacts object (-1 = past contact) |
| `annotation.state_affordance` | `[x, y, z, rx, ry, rz]` | 6D EEF state at contact frame |
| `annotation.affordance_box` | `[[x1, y1], [x2, y2]]` | Gripper bounding box at contact frame |
| `annotation.contact_points` | `[x, y]` | Contact point in pixel coordinates |
| `annotation.origin_shape` | `[h, w]` | Original image resolution for coordinate reference |

#### Bounding Box Format

All bounding boxes use pixel coordinates with origin at top-left:
```json
[[x1, y1], [x2, y2]]   // [top-left, bottom-right]
```

#### Trace Format

10 future waypoints for gripper trajectory prediction:
```json
[[110, 66], [112, 68], [115, 70], [118, 72], [120, 75], [122, 78], [125, 80], [128, 82], [130, 85], [132, 88]]
```

---

### Q_Annotation Fields (Quality Indicators, Shared)

Each annotation has a corresponding quality indicator prefixed with `Q_annotation.`:

| Field | Values | Description |
|-------|--------|-------------|
| `Q_annotation.instruction_add` | `"Primary"` / `"Secondary"` / `""` | Instruction quality |
| `Q_annotation.substask` | `"Primary"` / `"Secondary"` / `""` | Subtask quality |
| `Q_annotation.primitive_skill` | `"Primary"` / `"Secondary"` / `""` | Primitive skill quality |
| `Q_annotation.segmentation` | `"Primary"` / `"Secondary"` / `""` | Segmentation quality |
| `Q_annotation.object_box` | `"Primary"` / `"Secondary"` / `""` | Object box quality |
| `Q_annotation.placement_proposal` | `"Primary"` / `"Secondary"` / `""` | Placement proposal quality |
| `Q_annotation.trace` | `"Primary"` / `"Secondary"` / `""` | Trace quality |
| `Q_annotation.gripper_box` | `"Primary"` / `"Secondary"` / `""` | Gripper box quality |
| `Q_annotation.contact_frame` | `"Primary"` / `"Secondary"` / `""` | Contact frame quality |
| `Q_annotation.state_affordance` | `"Primary"` / `"Secondary"` / `""` | State affordance quality |
| `Q_annotation.affordance_box` | `"Primary"` / `"Secondary"` / `""` | Affordance box quality |
| `Q_annotation.contact_points` | `"Primary"` / `"Secondary"` / `""` | Contact points quality |

- **Primary**: High-confidence annotation
- **Secondary**: Acceptable quality, may have minor errors
- **""** (empty): No annotation available

---

## Download & Extract

The `data/` and `videos/` directories are distributed as `.tar` archives (one per chunk) to reduce the number of files during transfer. After downloading, extract them in place:

```bash
cd Annotation_with_action_lerobotv21

for dataset in lerobot_droid_anno lerobot_rh20t_anno; do
  for subdir in data videos; do
    cd ${dataset}/${subdir}
    for f in *.tar; do tar xf "$f" && rm "$f"; done
    cd ../..
  done
done
```

After extraction, each `data/` will contain `chunk-000/`, `chunk-001/`, ... with `.parquet` files, and each `videos/` will contain `chunk-000/`, `chunk-001/`, ... with `.mp4` files. The `meta/` directories are ready to use without extraction.


## Quick Start

The dataloader code is at [RoboInterData/lerobot_dataloader](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/lerobot_dataloader).

### Installation

```bash
pip install numpy torch pyarrow av opencv-python
```

### Basic Usage

```python
from lerobot_dataloader import create_dataloader

dataloader = create_dataloader(
    "path/to/lerobot_droid_anno",
    batch_size=32,
    action_horizon=16,
)

for batch in dataloader:
    images = batch["observation.images.primary"]  # (B, H, W, 3)
    actions = batch["action"]                     # (B, 16, 7)
    trace = batch["annotation.trace"]             # Parsed JSON lists
    skill = batch["annotation.primitive_skill"]   # List of strings
    break
```

### Multiple Datasets (DROID + RH20T)

```python
dataloader = create_dataloader(
    [
        "path/to/lerobot_droid_anno",
        "path/to/lerobot_rh20t_anno",
    ],
    batch_size=32,
    action_horizon=16,
)

for batch in dataloader:
    print(batch["dataset_name"])  # Source dataset identifier
    break
```

### Data Filtering

#### Frame Range Filtering

Remove idle frames at episode start/end using `range_nop.json`:

```python
dataloader = create_dataloader(
    "path/to/lerobot_droid_anno",
    range_nop_path="path/to/range_nop.json",
)
```

Format of `range_nop.json`:
```json
{
  "3072_exterior_image_1_left": [12, 217, 206]
}
```
`[start_frame, end_frame, valid_length]` — frames outside this range are idle/stationary.

#### Q_Annotation Filtering

Select episodes by annotation quality:

```python
from lerobot_dataloader import create_dataloader, QAnnotationFilter

# Only Primary quality
dataloader = create_dataloader(
    "path/to/lerobot_droid_anno",
    q_filters=[
        QAnnotationFilter("Q_annotation.instruction_add", ["Primary"]),
        QAnnotationFilter("Q_annotation.gripper_box", ["Primary"]),
    ]
)

# Any non-empty annotation
dataloader = create_dataloader(
    "path/to/lerobot_droid_anno",
    q_filters=[
        QAnnotationFilter("Q_annotation.trace", ["not_empty"])
    ]
)
```

#### Combined Filtering

```python
from lerobot_dataloader import FilterConfig, QAnnotationFilter

config = FilterConfig(
    range_nop_path="path/to/range_nop.json",
    q_filters=[
        QAnnotationFilter("Q_annotation.trace", ["Primary", "Secondary"]),
    ],
    q_filter_mode="all",  # "all" = AND, "any" = OR
)

dataloader = create_dataloader("path/to/lerobot_droid_anno", filter_config=config)
```

### Transforms

```python
from lerobot_dataloader import Compose, Normalize, ResizeImages, ToTensorImages, LeRobotDataset
from lerobot_dataloader.transforms import compute_stats

# Compute normalization stats
dataset = LeRobotDataset("path/to/lerobot_droid_anno", load_videos=False)
stats = compute_stats(dataset)

# Create transform pipeline
transform = Compose([
    ResizeImages(height=224, width=224),
    ToTensorImages(),   # (H,W,C) uint8 -> (C,H,W) float32
    Normalize(stats),
])

dataloader = create_dataloader("path/to/lerobot_droid_anno", transform=transform)
```

### Direct Dataset Access

```python
from lerobot_dataloader import LeRobotDataset
from lerobot_dataloader.transforms import ParseAnnotations

dataset = LeRobotDataset(
    "path/to/lerobot_droid_anno",
    transform=ParseAnnotations(),
)

print(f"Total frames: {len(dataset)}")
print(f"Total episodes: {dataset.num_episodes}")
print(f"FPS: {dataset.fps}")

sample = dataset[0]
print(f"Action: {sample['action']}")
print(f"Object box: {sample['annotation.object_box']}")
print(f"Skill: {sample['annotation.primitive_skill']}")
```

---

## Format Conversion

The LeRobot v2.1 format was converted from original data + LMDB annotations using:

- **DROID**: [convert_droid_to_lerobot_anno_fast.py](https://github.com/InternRobotics/RoboInter/blob/main/RoboInterData/convert_to_lerobot/convert_droid_to_lerobot_anno_fast.py)
- **RH20T**: [convert_rh20t_to_lerobot_anno_fast.py](https://github.com/InternRobotics/RoboInter/blob/main/RoboInterData/convert_to_lerobot/convert_rh20t_to_lerobot_anno_fast.py)

---

## Related Resources

| Resource | Link |
|----------|------|
| RoboInter-Data (parent dataset) | [HuggingFace](https://huggingface.co/datasets/InternRobotics/RoboInter-Data) |
| RoboInter Project | [GitHub](https://github.com/InternRobotics/RoboInter) |
| DataLoader Code | [lerobot_dataloader](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/lerobot_dataloader) |
| Conversion Scripts | [convert_to_lerobot](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/convert_to_lerobot) |
| Demo Visualizer | [RoboInterData-Demo](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData-Demo) |
| DROID Dataset | [droid-dataset.github.io](https://droid-dataset.github.io/) |
| RH20T Dataset | [rh20t.github.io](https://rh20t.github.io/) |

## License

Please refer to the original dataset licenses for [RoboInter](https://github.com/InternRobotics/RoboInter), [DROID](https://droid-dataset.github.io/), and [RH20T](https://rh20t.github.io/).