---
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).