README.md

---
pretty_name: RoboInter-Data
task_categories:
- robotics
language:
- en
tags:
- Embodied-AI
- Robotic manipulation
- intermediate-representation
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---

# RoboInter-Data: Intermediate Representation Annotations for Robot Manipulation

Rich, dense, per-frame **intermediate representation annotations** for robot manipulation, built on top of [DROID](https://droid-dataset.github.io/) and [RH20T](https://rh20t.github.io/). Developed as part of the [RoboInter](https://github.com/InternRobotics/RoboInter) project. You can try our [**Online demo**](https://huggingface.co/spaces/wz7in/robointer-demo).

The annotations cover 230k episodes and include: subtasks,
primitive skills, segmentation, gripper/object bounding boxes, placement proposals, affordance boxes,
grasp poses, traces, contact points, etc. And each with a quality rating (Primary / Secondary).

## Dataset Structure

```
RoboInter-Data/
│
├── Annotation_with_action_lerobotv21/    # [Main] LeRobot v2.1 format (actions + annotations + videos)
│   ├── lerobot_droid_anno/               #   DROID: 152,986 episodes
│   └── lerobot_rh20t_anno/              #   RH20T:  82,894 episodes
│
├── Annotation_pure/                      # Annotation-only LMDB (no actions/videos)
│   └── annotations/                      #   35 GB, all 235,920 episodes
│
├── Annotation_raw/                       # Original unprocessed annotations
│   ├── droid_annotation.pkl              #   Raw DROID annotations (~20 GB)
│   ├── rh20t_annotation.pkl              #   Raw RH20T annotations (~11 GB)
│   └── segmentation_npz.zip.*            #   Segmentation masks (~50 GB, split archives)
│
├── Annotation_demo_app/                  # Small demo subset for online visualization
│   ├── demo_data/                        #   LMDB annotations for 20 sampled videos
│   └── videos/                           #   20 MP4 videos
│
├── Annotation_demo_larger/               # Larger demo subset for local visualization
│   ├── demo_annotations/                 #   LMDB annotations for 120 videos
│   └── videos/                           #   120 MP4 videos
│
├── All_Keys_of_Primary.json              # Episode names where all annotations are Primary quality
├── RoboInter_Data_Qsheet.json            # Per-episode quality ratings for each annotation type
├── RoboInter_Data_Qsheet_value_stats.json# Distribution statistics of quality ratings
├── RoboInter_Data_RawPath_Qmapping.json  # Mapping: original data source path -> episode splits & quality
├── range_nop.json                        # Non-idle frame ranges for all 230k episodes
├── range_nop_droid_all.json              # Non-idle frame ranges (DROID only)
├── range_nop_rh20t_all.json              # Non-idle frame ranges (RH20T only)
├── val_video.json                        # Validation set: 7,246 episode names
└── VideoID_2_SegmentationNPZ.json        # Episode video ID -> segmentation NPZ file path mapping
```

---

## 1. Annotation_with_action_lerobotv21 (Recommended)

The primary data format. Contains **actions + observations + annotations** in [LeRobot v2.1](https://github.com/huggingface/lerobot) format (parquet + MP4 videos), ready for policy training.

### Download & Extract
Dataset [link](https://huggingface.co/datasets/InternRobotics/RoboInter-Data/tree/main/Annotation_with_action_lerobotv21). Dataloader Code [link](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData).
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.

### Directory Layout

```
lerobot_droid_anno/  (or lerobot_rh20t_anno/)
├── meta/
│   ├── info.json              # Dataset metadata (fps=10, features, etc.)
│   ├── episodes.jsonl         # Episode information
│   └── tasks.jsonl            # Task/instruction mapping
├── data/
│   └── chunk-{NNN}/           # Parquet files (1,000 episodes per chunk)
│       └── episode_{NNNNNN}.parquet
└── videos/
    └── chunk-{NNN}/
        ├── observation.images.primary/
        │   └── episode_{NNNNNN}.mp4
        └── observation.images.wrist/
            └── episode_{NNNNNN}.mp4
```

### Data Fields

| Category | Field | Shape / Type | Description |
|----------|-------|-------------|-------------|
| **Core** | `action` | (7,) float64 | Delta EEF: [dx, dy, dz, drx, dry, drz, gripper] |
| | `state` | (7,) float64 | EEF state: [x, y, z, rx, ry, rz, gripper] |
| | `observation.images.primary` | (180, 320, 3) video | Primary camera RGB |
| | `observation.images.wrist` | (180, 320, 3) video | Wrist camera RGB |
| **Annotation** | `annotation.instruction_add` | string | Structured task language instruction |
| | `annotation.substask` | string | Current subtask description |
| | `annotation.primitive_skill` | string | Primitive skill label (pick, place, push, ...) |
| | `annotation.object_box` | JSON `[[x1,y1],[x2,y2]]` | Manipulated object bounding box |
| | `annotation.gripper_box` | JSON `[[x1,y1],[x2,y2]]` | Gripper bounding box |
| | `annotation.trace` | JSON `[[x,y], ...]` | Future 10-step gripper trajectory |
| | `annotation.contact_frame` | JSON int | Frame index when gripper contacts object |
| | `annotation.contact_points` | JSON `[x, y]` | Contact point pixel coordinates |
| | `annotation.affordance_box` | JSON `[[x1,y1],[x2,y2]]` | Gripper box at contact frame |
| | `annotation.state_affordance` | JSON `[x,y,z,rx,ry,rz]` | 6D EEF state at contact frame |
| | `annotation.placement_proposal` | JSON `[[x1,y1],[x2,y2]]` | Target placement bounding box |
| | `annotation.time_clip` | JSON `[[s,e], ...]` | Subtask temporal segments |
| **Quality** | `Q_annotation.*` | string | Quality rating: `"Primary"` / `"Secondary"` / `""` |

### Quick Start
The dataloader is located at our RoboInter [Codebase](https://github.com/InternRobotics/RoboInter/blob/main/RoboInterData/lerobot_dataloader).

```python
from lerobot_dataloader import create_dataloader

# Single dataset
dataloader = create_dataloader(
    "path/to/Annotation_with_action_lerobotv21/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"]              # JSON strings
    skill = batch["annotation.primitive_skill"]    # List[str]
    break

# Multiple datasets (DROID + RH20T)
dataloader = create_dataloader(
    [
        "path/to/lerobot_droid_anno",
        "path/to/lerobot_rh20t_anno",
    ],
    batch_size=32,
    action_horizon=16,
)
```

### Filtering by Quality & Frame Range

```python
from lerobot_dataloader import create_dataloader, QAnnotationFilter

dataloader = create_dataloader(
    "path/to/lerobot_droid_anno",
    batch_size=32,
    range_nop_path="path/to/range_nop.json",       # Remove idle frames
    q_filters=[
        QAnnotationFilter("Q_annotation.trace", ["Primary"]),
        QAnnotationFilter("Q_annotation.gripper_box", ["Primary", "Secondary"]),
    ],
)
```

For full dataloader documentation and transforms, see: [RoboInterData/lerobot_dataloader](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/lerobot_dataloader).

### Format Conversion Scripts

The LeRobot v2.1 data was converted 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)

---

## 2. Annotation_pure (Annotation-Only LMDB)

Contains **only the intermediate representation annotations** (no action data, no videos) stored as a single LMDB database. Useful for lightweight access to annotations or as input for the LeRobot conversion pipeline. The format conversion scripts and corresponding lightweight dataloader functions are provided in [lmdb_tool](https://github.com/InternRobotics/RoboInter/blob/main/RoboInterData/lmdb_tool). You can downloade high-resolution
videos by following [Droid hr_video_reader](https://github.com/InternRobotics/RoboInter/blob/main/RoboInterData/hr_video_reader) and [RH20T API](https://github.com/rh20t/rh20t_api).

### Data Format

Each LMDB key is an episode name (e.g., `"3072_exterior_image_1_left"`). The value is a dict mapping frame indices to per-frame annotation dicts:

```python
{
    0: {  # frame_id
        "time_clip": [[0, 132], [132, 197], [198, 224]],   # subtask segments
        "instruction_add": "pick up the red cup",           # language instruction
        "substask": "reach for the cup",                    # current subtask
        "primitive_skill": "reach",                         # skill label
        "segmentation": None,                               # (stored separately in Annotation_raw)
        "object_box": [[45, 30], [120, 95]],                # manipulated object bbox
        "placement_proposal": [[150, 80], [220, 140]],      # target placement bbox
        "trace": [[x, y], ...],                             # next 10 gripper waypoints
        "gripper_box": [[60, 50], [100, 80]],               # gripper bbox
        "contact_frame": 101,                               # contact event frame (−1 if past contact)
        "state_affordance": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],# 6D EEF state at contact
        "affordance_box": [[62, 48], [98, 82]],             # gripper bbox at contact frame
        "contact_points": [[75, 65], [85, 65]],             # contact pixel coordinates
        ...
    },
    1: { ... },
    ...
}
```

### Reading LMDB

```python
import lmdb
import pickle

lmdb_path = "Annotation_pure/annotations"
env = lmdb.open(lmdb_path, readonly=True, lock=False, readahead=False)

with env.begin() as txn:
    # List all episode keys
    cursor = txn.cursor()
    for key, value in cursor:
        episode_name = key.decode("utf-8")
        episode_data = pickle.loads(value)

        # Access frame 0
        frame_0 = episode_data[0]
        print(f"{episode_name}: {frame_0['instruction_add']}")
        print(f"  object_box: {frame_0['object_box']}")
        print(f"  trace: {frame_0['trace'][:3]}...")  # first 3 waypoints
        break

env.close()
```

### CLI Inspection Tool

```bash
cd RoboInter/RoboInterData/lmdb_tool

# Basic info
python read_lmdb.py --lmdb_path Annotation_pure/annotations --action info

# View a specific episode
python read_lmdb.py --lmdb_path Annotation_pure/annotations --action item --key "3072_exterior_image_1_left"

# Field coverage statistics
python read_lmdb.py --lmdb_path Annotation_pure/annotations --action stats --key "3072_exterior_image_1_left"

# Multi-episode summary
python read_lmdb.py --lmdb_path Annotation_pure/annotations --action summary --limit 100
```

---

## 3. Annotation_raw (Original Annotations)

The original, unprocessed annotation files before conversion to LMDB format. These files are large and slow to load.

| File | Size | Description |
|------|------|-------------|
| `droid_annotation.pkl` | ~20 GB | Raw DROID intermediate representation annotations |
| `rh20t_annotation.pkl` | ~11 GB | Raw RH20T intermediate representation annotations |
| `segmentation_npz.zip.*` | ~50 GB | Object segmentation masks (split archives) |

### Reading Raw PKL
```bash
cd /RoboInter-Data/Annotation_raw
cat segmentation_npz.zip.* > segmentation_npz.zip
unzip segmentation_npz.zip
```

```python
import pickle

with open("Annotation_raw/droid_annotation.pkl", "rb") as f:
    droid_data = pickle.load(f)  # Warning: ~20 GB, takes several minutes

# droid_data[episode_key] contains raw intermediate representation data
# including: all_language, all_gripper_box, all_grounding_box, all_contact_point, all_traj, etc.
```

> To convert raw PKL to the LMDB format used in `Annotation_pure`, see the conversion script in the [RoboInter repository](https://github.com/InternRobotics/RoboInter).

---

## 4. Demo Subsets (Annotation_demo_app & Annotation_demo_larger)

Pre-packaged subsets for quick visualization using the [RoboInterData-Demo](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData-Demo) Gradio app. Both subsets share the same LMDB annotation format + MP4 video structure.

| Subset | Videos | Size | Use Case |
|--------|--------|------|----------|
| `Annotation_demo_app` | 20 | ~929 MB | HuggingFace Spaces [online demo](https://huggingface.co/spaces/wz7in/robointer-demo) |
| `Annotation_demo_larger` | 120 | ~12 GB | Local visualization with more examples |

### Running the Visualizer

```bash
git clone https://github.com/InternRobotics/RoboInter.git
cd RoboInter/RoboInterData-Demo

# Option A: Use the small demo subset (for Spaces)
ln -s /path/to/Annotation_demo_app/demo_data ./demo_data
ln -s /path/to/Annotation_demo_app/videos ./videos

# Option B: Use the larger demo subset (for local)
ln -s /path/to/Annotation_demo_larger/demo_annotations ./demo_data
ln -s /path/to/Annotation_demo_larger/videos ./videos

pip install -r requirements.txt
python app.py
# Open http://localhost:7860
```

The visualizer supports all annotation types: object segmentation masks, gripper/object/affordance bounding boxes, trajectory traces, contact points, grasp poses, and language annotations (instructions, subtasks, primitive skills).

---

## 5. Metadata JSON Files

### Quality & Filtering

| File | Description |
|------|-------------|
| `All_Keys_of_Primary.json` | List of 65,515 episode names where **all** annotation types are rated Primary quality. |
| `RoboInter_Data_Qsheet.json` | Per-episode quality ratings for every annotation type. Each entry contains `Q_instruction_add`, `Q_substask`, `Q_trace`, etc. with values `"Primary"`, `"Secondary"`, or `null`. |
| `RoboInter_Data_Qsheet_value_stats.json` | Distribution of quality ratings across all episodes. |
| `RoboInter_Data_RawPath_Qmapping.json` | Mapping from original data source paths to episode splits and their quality ratings. |

### Frame Ranges (Idle Frame Removal)

| File | Description |
|------|-------------|
| `range_nop.json` | Non-idle frame ranges for all 235,920 episodes (DROID + RH20T). |
| `range_nop_droid_all.json` | Non-idle frame ranges for DROID episodes only. |
| `range_nop_rh20t_all.json` | Non-idle frame ranges for RH20T episodes only. |

Format: `{ "episode_name": [start_frame, end_frame, valid_length] }`

```python
import json

with open("range_nop.json") as f:
    range_nop = json.load(f)

# Example: "3072_exterior_image_1_left": [12, 217, 206]
# Means: valid action frames are 12~217, total 206 valid frames
# (frames 0~11 and 218+ are idle/stationary)
```

### Other

| File | Description |
|------|-------------|
| `val_video.json` | List of 7,246 episode names reserved for the validation set. |
| `VideoID_2_SegmentationNPZ.json` | Mapping from episode video ID to the corresponding segmentation NPZ file path in `Annotation_raw/segmentation_npz`. `null` if no segmentation is available. |

---

## Related Resources

| Resource | Link |
|----------|------|
| Project | [RoboInter](https://github.com/InternRobotics/RoboInter) |
| VQA Dataset | [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) |
| VLM Checkpoints | [RoboInter-VLM](https://huggingface.co/InternRobotics/RoboInter-VLM) |
| LMDB Tool | [RoboInterData/lmdb_tool](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/lmdb_tool) |
| High-Resolution Video Reader | [RoboInterData/hr_video_reader](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/hr_video_reader) |
| LeRobot DataLoader | [RoboInterData/lerobot_dataloader](https://github.com/InternRobotics/RoboInter/tree/main/RoboInterData/lerobot_dataloader) |
| LeRobot Conversion | [RoboInterData/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) |
| Online Demo | [HuggingFace Space](https://huggingface.co/spaces/wz7in/robointer-demo) |
| Raw DROID Dataset | [droid-dataset.github.io](https://droid-dataset.github.io/) |
| Raw RH20T Dataset | [rh20t.github.io](https://rh20t.github.io/) |

## Citation

If you find RoboInter useful in your research, please consider citing:

```bibtex
@article{li2026robointer,
  title={RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation},
  author={Li, Hao and Wang, Ziqin and Ding, Zi-han and Yang, Shuai and Chen, Yilun and Tian, Yang and Hu, Xiaolin and Wang, Tai and Lin, Dahua and Zhao, Feng and others},
  journal={arXiv preprint arXiv:2602.09973},
  year={2026}
}
```

## 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/).