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pretty_name: Orchestra-Bench
language:
- en
task_categories:
- image-to-text
tags:
- robotics
- multi-robot-coordination
- vision-language-models
- embodied-ai
size_categories:
- 10K<n<100K
---
# Orchestra-Bench
Orchestra-Bench is an English multimodal dataset for high-level cooperative planning among three robots. Each sample provides three views from distinct positions in one scene, a broad user request, and one coordinated text subtask for each robot.
## Dataset Overview
- 12,000 samples across 24 scene classes, with 500 samples per class
- 36,000 image references backed by 3,334 unique extracted frames
- Three distinct ground views per sample: `ground_1`, `ground_2`, and `ground_3`
- 17 outdoor classes (8,500 samples) and 7 indoor classes (3,500 samples)
- English user tasks and high-level cooperative robot subtasks
- 14 outdoor source scenes from EgoSchema, contributing 7,000 samples
- Real video frames only; no synthetic or perspective-warped BEV images
| Environment | Scene class | Samples |
| --- | --- | ---: |
| Outdoor | Park path | 500 |
| Outdoor | Park junction | 500 |
| Outdoor | Building approach | 500 |
| Outdoor | EgoSchema golf course 01 | 500 |
| Outdoor | EgoSchema golf course 02 | 500 |
| Outdoor | EgoSchema tennis court | 500 |
| Outdoor | EgoSchema lawn equipment yard | 500 |
| Outdoor | EgoSchema garden work area | 500 |
| Outdoor | EgoSchema night road | 500 |
| Outdoor | EgoSchema sports field | 500 |
| Outdoor | EgoSchema residential road | 500 |
| Outdoor | EgoSchema public plaza | 500 |
| Outdoor | EgoSchema garden path | 500 |
| Outdoor | EgoSchema sidewalk | 500 |
| Outdoor | EgoSchema urban walkway | 500 |
| Outdoor | EgoSchema grassy field | 500 |
| Outdoor | EgoSchema dirt field | 500 |
| Indoor | Office and laboratory | 500 |
| Indoor | Retail and dining | 500 |
| Indoor | Bedroom | 500 |
| Indoor | Kitchen | 500 |
| Indoor | Living room | 500 |
| Indoor | Bathroom | 500 |
| Indoor | Corridor | 500 |
## Record Format
The main file is `manifest.jsonl`. Each line has this structure:
```json
{
"id": "egoschema_dirt_field_0000",
"scene_class": "egoschema_dirt_field",
"environment": "outdoor",
"scene_description": "Outdoor dirt field with uneven ground and sparse vegetation",
"source_scene": "egoschema/eed1a49f-ba2e-4b83-8817-d8b5d77a3b42",
"collaboration_pattern": "forward scouting and follow-up",
"views": [
{
"robot_id": "ground_1",
"view_type": "ground",
"image": "images/egoschema_dirt_field/egoschema_dirt_field_0000/ground_1.jpg",
"source_video": "egoschema_eed1a49f-ba2e-4b83-8817-d8b5d77a3b42",
"timestamp_seconds": 22.0
}
],
"user_task": "We need to clear a path through the uneven dirt field to reach the target zone ahead.",
"subtasks": {
"ground_1": "Move forward to inspect the left side of the path for obstacles or unstable ground.",
"ground_2": "Approach from behind and check the dirt for soft spots.",
"ground_3": "Advance while clearing debris and marking the route for the others."
}
}
```
Every robot subtask includes an explicit movement action. Plans cover navigation, observation, route checking, blind-spot coverage, reporting, and mutual guidance at a high level rather than low-level controls or trajectories.
Image paths in `views` are relative to the dataset repository root. Multiple records can reuse a source frame, but the three-view combination within every record is distinct.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset(
"BAAI/Orchestra-Bench",
data_files="manifest.jsonl",
split="train",
)
print(dataset[0])
```
This dataset is the result of joint research conducted by Hao Tang's team at the School of Computer Science, Peking University, and the Beijing Academy of Artificial Intelligence (BAAI).
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