Research Blog on March 10 | Technical Report

AgiBot World Colosseo is a full-stack large-scale robot learning platform curated for advancing bimanual manipulation in scalable and intelligent embodied systems. It is accompanied by foundation models, benchmarks, and an ecosystem to democratize access to high-quality robot data for the academic community and the industry, paving the path towards the "ImageNet Moment" for Embodied AI.

We have released:

• Task Catalog: Reference sheet outlining the tasks in our dataset, including robot end-effector types, sample action-text descriptions and more
• AgiBot World Beta: Our complete dataset featuring 1,003,672 trajectories (~43.8T)
• AgiBot World Alpha: A curated subset of AgiBot World Beta, containing 92,214 trajectories (~8.5T)

News📰

• [2025/03/10] 📄 Research Blog and Technical Report released.
• [2025/03/01] Agibot World Beta released.
• [2025/01/03] Agibot World Alpha Sample Dataset released.
• [2024/12/30] 🤖 Agibot World Alpha released.

TODO List 📅

• AgiBot World Alpha
• AgiBot World Beta (expected Q1 2025)
  • ~1,000,000 trajectories of high-quality robot data
• AgiBot World Foundation Model: GO-1 (expected Q2 2025)
  • Training & inference code
  • Pretrained model checkpoint
• AgiBot World Colosseo (expected 2025)
  • A comprehensive platform with toolkits including teleoperation, training and inference.
• 2025 AgiBot World Challenge (expected 2025)

Key Features 🔑

• 1 million+ trajectories from 100 robots.
• 100+ 1:1 replicated real-life scenarios across 5 target domains.
• Cutting-edge hardware: visual tactile sensors / 6-DoF Dexterous hand / mobile dual-arm robots
• Wide-spectrum versatile challenging tasks

Contact-rich Manipulation

Long-horizon Planning

Multi-robot Collaboration

Table of Contents

1. Key Features
2. At a Quick Glance
3. Getting Started
   • Installation
   • How to Get Started with Our AgiBot World Data
   • Visualize Datasets
   • Policy Learning Quickstart
4. TODO List
5. License and Citation

At a Quick Glance⬇️

Follow the steps below to quickly explore and get an overview of AgiBot World with our sample dataset (~7GB).

# Installation
conda create -n agibotworld python=3.10 -y
conda activate agibotworld
pip install git+https://github.com/huggingface/lerobot@59e275743499c5811a9f651a8947e8f881c4058c
pip install matplotlib
git clone https://github.com/OpenDriveLab/AgiBot-World.git
cd AgiBot-World

# Download the sample dataset (~7GB) from Hugging Face. Replace <your_access_token> with your Hugging Face Access Token. You can generate an access token by following the instructions in the Hugging Face documentation from https://huggingface.co/docs/hub/security-tokens
mkdir data
cd data
curl -L -o sample_dataset.tar -H "Authorization: Bearer <your_access_token>" https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha/resolve/main/sample_dataset.tar
tar -xvf sample_dataset.tar

# Convert the sample dataset to LeRobot dataset format and visualize
cd ..
python scripts/convert_to_lerobot.py --src_path ./data/sample_dataset --task_id 390 --tgt_path ./data/sample_lerobot
python scripts/visualize_dataset.py --task-id 390 --dataset-path ./data/sample_lerobot

Getting started 🔥

Installation

Download our source code:

git clone https://github.com/OpenDriveLab/AgiBot-World.git
cd AgiBot-World

Our project is built upon the lerobot library (dataset v2.0, commit 59e2757), install lerobot through

pip install git+https://github.com/huggingface/lerobot@59e275743499c5811a9f651a8947e8f881c4058c

How to Get Started with Our AgiBot World Data

• [OPTION 1] Download data from our OpenDataLab page.

pip install openxlab # install CLI
openxlab dataset get --dataset-repo OpenDriveLab/AgiBot-World # dataset download

• [OPTION 2] Download data from our HuggingFace page.

huggingface-cli download --resume-download --repo-type dataset agibot-world/AgiBotWorld-Alpha --local-dir ./AgiBotWorld-Alpha

Convert the data to LeRobot Dataset format.

python scripts/convert_to_lerobot.py --src_path /path/to/agibotworld/alpha --task_id 390 --tgt_path /path/to/save/lerobot

Visualize Datasets

We adapt and extend the dataset visualization script from LeRobot Project

python scripts/visualize_dataset.py --task-id 390 --dataset-path /path/to/lerobot/format/dataset

It will open rerun.io and display the camera streams, robot states and actions, like this:

Policy Training Quickstart

Leveraging the simplicity of LeRobot Dataset, we provide a user-friendly Jupyter Notebook for training diffusion policy on AgiBot World Dataset.

License and Citation📄

All the data and code within this repo are under CC BY-NC-SA 4.0.

• Please consider citing our work if it helps your research.
• For the full authorship and detailed contributions, please refer to contributions.
• In alphabetical order by surname:

@article{bu2025agibot,
  title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},
  author={Bu, Qingwen and Cai, Jisong and Chen, Li and Cui, Xiuqi and Ding, Yan and Feng, Siyuan and Gao, Shenyuan and He, Xindong and Huang, Xu and Jiang, Shu and others},
  journal={arXiv preprint arXiv:2503.06669},
  year={2025}
}