---
language:
- en
- zh
pretty_name: MultiDojo²
size_categories:
- 1K<n<10K
task_categories:
- robotics
tags:
- robotics
- multi-robot
- manipulation
- heterogeneous-robots
- simulation
- world-action-model
- multi-view
viewer: false
---

# MultiDojo²

[English](#overview) · [简体中文](#中文说明) · [Project website](https://worldagents-c.github.io/MultiDojo2/index.en.html) · [GitHub](https://github.com/worldAgents-c/MultiDojo2)

## Overview

**2,000+ simulation demonstrations across 20+ multi-robot manipulation tasks.** The collection covers homogeneous and heterogeneous teams of 2, 3, 4, and 8 arms, including handovers, relays, collaborative transport, ordered stacking, and parallel manipulation. Data is released progressively on this repository.

Every episode includes synchronized **first-person views for every arm, third-person views, robot states and actions, and camera calibration**. Multi-station tasks also include station views. RGB streams retain their recorded **640×480 resolution at 30 Hz**.

## Download and load

```bash
pip install -U huggingface_hub
hf download WorldAgents-c/MultiDojo2 --repo-type dataset --local-dir multidojo-data
cd multidojo-data
pip install -r requirements.txt
```

The currently downloadable files total approximately **671.6 GiB**. Loading requires Python and the packages in requirements.txt; a simulator or GPU is unnecessary.

```python
from loader import TrainingDataset

dataset = TrainingDataset("train.jsonl", max_open=2)
sample = dataset.step(episode=0, frame=0, action_horizon=8)
print(sample["instruction"])
print(sample["images"].keys())
print(sample["robots"].keys())
dataset.close()
```

For a partial download, include loader.py, requirements.txt, and every file in each selected episode directory, then filter train.jsonl to those episodes. Paths are relative to the index file. Directory numbers preserve the collection's original numbering; **tasks.json maps every task to its episode directories and index rows**.

## Files

- `train.jsonl` and `tasks.json`: accepted episodes, task instructions, view names, and task-to-directory mapping.
- `episodes/NNNN/render/lmdb/data.mdb`: synchronized RGB, state, action, object-pose, and timing arrays.
- `episodes/NNNN/render/meta_info.pkl`: robot joint metadata, camera descriptions, and LMDB key mapping.
- `episodes/NNNN/render/camera-calibration.npz`: camera world poses and calibration parameters.
- `episodes/NNNN/trajectory/trajectory.npz`: native numeric trajectories, object poses, and robot link transforms.

Actions are **absolute native joint-position commands**, with radians or metres specified by each robot's joint metadata. Observation/state at t precede action t; next observations/state correspond to t+1. Variable joint dimensions, passive masks, and action chunks are supported. See [FORMAT.md](FORMAT.md) for details.

## Tasks

| ID | Task | Team | Demonstrations |
| --- | --- | --- | ---: |
| 00 | Banana handover into a pan | Homogeneous · 2 arms | 100 |
| 01 | Banana and apple to plates | Homogeneous · 2 arms | 100 |
| 02 | Collaborative carton transport | Homogeneous · 2 arms | 100 |
| 03 | Two-stage cup relay | Homogeneous · 2 arms | 100 |
| 04 | Place and stack blocks | Homogeneous · 2 arms | 100 |
| 05 | Franka–Piper long-block handover | Heterogeneous · 2 arms | 100 |
| 06 | Three-arm block relay | Homogeneous · 3 arms | 100 |
| 07 | Apples to baskets | Homogeneous · 4 arms | 100 |
| 08 | Four-arm block relay | Homogeneous · 4 arms | 100 |
| 09 | Ordered four-color stacking | Homogeneous · 4 arms | 100 |
| 10 | Cookies to boxes | Homogeneous · 4 arms | 100 |
| 11 | Household objects to boxes | Homogeneous · 4 arms | 100 |
| 12 | Eight-arm blocks to boxes | Homogeneous · 8 arms | 100 |
| 13 | Three-arm block relay | Heterogeneous · 3 arms | 100 |
| 14 | Four-arm block relay | Heterogeneous · 4 arms | 100 |
| 15 | Eight-arm blocks to boxes | Heterogeneous · 8 arms | 100 |
| 16 | Three-arm banana relay | Heterogeneous · 3 arms | 100 |
| 17 | Three-arm white-cup relay | Heterogeneous · 3 arms | 100 |
| 18 | Four-arm banana relay | Heterogeneous · 4 arms | 100 |
| 19 | Four-arm white-cup relay | Heterogeneous · 4 arms | 100 |

## Attribution

These are simulation recordings. Simulator executables and source robot/object assets are not bundled. See [THIRD_PARTY_ATTRIBUTIONS.md](THIRD_PARTY_ATTRIBUTIONS.md) and [RIGHTS_STATUS.md](RIGHTS_STATUS.md).

## 中文说明

MultiDojo² 数据集包含 **2,000+ 条训练轨迹、20+ 类多机器人协作任务**，覆盖同构与异构的双臂、三臂、四臂和八臂组合。训练数据在本仓库持续开放。

每条轨迹同步保存**每个机械臂的第一视角、第三视角、状态与动作，以及相机标定**；多工位任务还包含工位视角。RGB 保持原始 640×480、30 Hz。当前可下载文件约 **671.6 GiB**。

使用 train.jsonl 加载，tasks.json 提供每类任务对应的目录和索引行。目录编号沿用原始编号。安装 requirements.txt 后可使用 loader.py 读取。状态动作的单位、同步关系和仿真建模说明见 FORMAT.md。