![agibot_world](https://github.com/user-attachments/assets/df64b543-db82-41ee-adda-799970e8a198) Research Blog: GO-1 Open-sourcing | Research Blog: AgiBot World Colosseo | Technical Report

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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: - **GO-1:** Our robotic foundation model pretrained on AgiBot World Dataset - **GO-1 Air:** GO-1 model without Latent Planner, high-performanced and lightweighted - **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📰 > [!IMPORTANT] > 🌟 Stay up to date at [opendrivelab.com](https://opendrivelab.com/#news)! - **`[2025/09/19]`** 🚀 **Our robotic foundation model GO-1 open-sourced.** - **`[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 📅 - [x] **AgiBot World Alpha** - [x] **AgiBot World Beta** - [x] ~1,000,000 trajectories of high-quality robot data - [x] **AgiBot World Foundation Model: GO-1** - [x] GO-1 fine-tuning script - [x] GO-1 Air pre-trained checkpoint - [x] GO-1 pre-trained checkpoint - [x] Examples of using GO-1 model - [x] **2025 AgiBot World Challenge** ## 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** - **General robotic policy pretrained on AgiBot World**

Contact-rich Manipulation	Long-horizon Planning	Multi-robot Collaboration
Fold Shirt (AgileX)	Fold Shirt (AgiBot G1)	Fold Shirt (Dual Franka)

## Table of Contents - [News📰 ](#news-) - [TODO List 📅 ](#todo-list--) - [Key Features 🔑 ](#key-features--) - [Table of Contents](#table-of-contents) - [Getting started 🔥 ](#getting-started--) - [Installation ](#installation-) - [How to Get Started with Our AgiBot World Data ](#how-to-get-started-with-our-agibot-world-data-) - [Download Datasets ](#download-datasets-) - [Visualize Datasets ](#visualize-datasets-) - [How to Get Started with Our GO-1 Model ](#how-to-get-started-with-our-go-1-model-) - [Requirements ](#requirements-) - [Model Zoo ](#model-zoo-) - [Fine-tuning on Your Own Dataset ](#fine-tuning-on-your-own-dataset-) - [Testing Your Model ](#testing-your-model-) - [More Examples ](#more-examples-) - [License and Citation📄 ](#license-and-citation---) ## Getting started 🔥 ### Installation 1. Download our source code: ```bash git clone https://github.com/OpenDriveLab/AgiBot-World.git cd AgiBot-World ``` 2. Create a new conda environment: ```bash conda create -n go1 python=3.10 -y conda activate go1 ``` 3. Install dependencies: > This project is built on [LeRobot](https://github.com/huggingface/lerobot) (**dataset `v2.1`, commit `2b71789`**) > ⚡️ Our environment has been tested with **CUDA 12.4**. ```bash pip install -e . ``` Flash Attention is loaded through the [`kernels`](https://github.com/huggingface/kernels) library (installed by the command above), which fetches pre-built Flash Attention 2 binaries on first use — no local compilation required. The binaries are bit-exact with the upstream source build. If your machine is not covered by the pre-built binaries, the code automatically falls back to a source build of `flash-attn`, which you can install with: ```bash pip install --no-build-isolation flash-attn==2.4.2 ``` If you encounter out of RAM issue while installing [flash attention](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features), you can set the environment variable `MAX_JOBS` to limit the number of parallel compilation jobs: ```bash MAX_JOBS=4 pip install --no-build-isolation flash-attn==2.4.2 ``` ### How to Get Started with Our AgiBot World Data #### Download Datasets - [OPTION 1] Download data from our [OpenDataLab](https://opendatalab.com/OpenDriveLab/AgiBot-World) page. ```bash pip install openxlab # install CLI openxlab dataset get --dataset-repo OpenDriveLab/AgiBot-World # dataset download ``` - [OPTION 2] Download data from our [HuggingFace](https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha) page. ```bash huggingface-cli download --resume-download --repo-type dataset agibot-world/AgiBotWorld-Alpha --local-dir ./AgiBotWorld-Alpha ``` Convert the data to **LeRobot Dataset** format following [any4lerobot](https://github.com/Tavish9/any4lerobot). #### Visualize Datasets We adapt and extend the dataset visualization script from [LeRobot Project](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/visualize_dataset.py): ```bash 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:

### How to Get Started with Our GO-1 Model #### Requirements We strongly recommend full fine-tuning for the best performance. However, if GPU memory is limited, you can alternatively fine-tune only the Action Expert. | Usage | GPU Memory Required | Example GPU | | :-------------------: | :-------------------: | :-----------------: | | Inference | ~7GB | RTX 4090 | | Fine-tuning (Full) | ~70GB (batch size=16) | A100 80GB, H100 | | Fine-tuning (Only AE) | ~24GB (batch size=16) | RTX 4090, A100 40GB | #### Model Zoo | Model | HF Link | Description | | :------: | :------------------------------------------: | :-------------------------------------------------------------------: | | GO-1 Air | https://huggingface.co/agibot-world/GO-1-Air | GO-1 model without Latent Planner pre-trained on AgiBot World dataset | | GO-1 | https://huggingface.co/agibot-world/GO-1 | GO-1 model pre-trained on AgiBot World dataset | #### Fine-tuning on Your Own Dataset Here we provide an example of fine-tuning the GO-1 model on the [LIBERO](https://libero-project.github.io/intro.html) dataset. You can easily adapt it for your own data. **1. Prepare Data** We use the LeRobot dataset for our default dataset and dataloader. We provide a script for converting LIBERO to LeRobot format in [evaluate/libero/convert_libero_data_to_lerobot.py](evaluate/libero/convert_libero_data_to_lerobot.py). Since TensorFlow is required to read the [RLDS format](https://github.com/google-research/rlds), we recommend creating a separate conda environment to avoid package conflicts: ```bash conda create -n libero_data python=3.10 -y conda activate libero_data pip install -e ".[libero_data]" ``` Download the raw LIBERO dataset from [OpenVLA](https://huggingface.co/datasets/openvla/modified_libero_rlds), then run the script to convert it into LeRobot dataset: ```bash # Optional: Change the LeRobot home directory export HF_LEROBOT_HOME=/path/to/your/lerobot python evaluate/libero/convert_libero_data_to_lerobot.py --data_dir /path/to/your/libero/data ``` **2. Prepare Configs** We provide an example config for fine-tuning GO-1 on LIBERO in [go1/configs/go1_sft_libero.py](go1/configs/go1_sft_libero.py). Key sections in the config: - `DatasetArguments` - path or repo for the LeRobot dataset. - `GOModelArguments` - model settings: architecture (GO-1 Air or GO-1), action chunk size, diffusion scheduler, parameter freezing, etc. - `GOTrainingArguments` - training hyper-parameters, see [transformers docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) for more details. - `SpaceArguments` - state/action dimensions, data keys in the LeRobot dataset, default language prompt, control frequency. See [go1/configs/go1_base_cfg.py](go1/configs/go1_base_cfg.py) for all available config options. **3. Start Fine-tuning** Start fine-tuning with the following command, you can setup environment variables according to the [shell](go1/shell/train.sh). ```bash RUNNAME= bash go1/shell/train.sh /path/to/your/config ``` Checkpoints will be saved in `experiment/` and logs will be saved in `experiment//logs`. **Notes:** - We also provide a [debugging shell](go1/shell/train_dev.sh) which can run on a single RTX4090. It also set `DEBUG_MODE` to true for faster init. - We do not need to precompute the normalization statistics for the training data, as LeRobot will compute them when loading the dataset. The statistics will be saved to `experiment//dataset_stats.json`. - We set action chunk size and control frequency input as 30 in GO-1 pre-training, as our AgiBot World dataset is collected at 30Hz. We change them to 10 in LIBERO fine-tuning, as the LIBERO dataset is collected at 10Hz. You can change them accordingly in the config file. #### Testing Your Model **Local Inference** After fine-tuning, you can test your model locally using an example script in [evaluate/deploy.py](evaluate/deploy.py). You can build a `GO1Infer` object to load the model and dataset statistics, then call the `inference` method to run inference: ```python import numpy as np from evaluate.deploy import GO1Infer model = GO1Infer(model_path="/path/to/your/checkpoint", data_stats_path="/path/to/your/dataset_stats.json") payload = { "top": ..., "right": ..., "left": ..., "instruction": "example instruction", "state": ..., "ctrl_freqs": np.array([30]), } actions = model.inference(payload) ``` We also provide a script for open-loop evaluation with training data in [evaluate/openloop_eval.py](evaluate/openloop_eval.py). **Remote Inference** Considering that 1. real robot may not have powerful GPUs, 2. different robots and simulation benchmarks often require different package dependencies, we also provide a policy server for GO-1. A client in another environment or another machine send observations to the server for remote inference. Start the server and it will listen on port `PORT` and waits for observations: ```bash python evaluate/deploy.py --model_path /path/to/your/checkpoint --data_stats_path /path/to/your/dataset_stats.json --port ``` For the client, we provide a `GO1Client` class to send requests to the server and receive actions: ```python from typing import Dict, Any import json_numpy import numpy as np import requests json_numpy.patch() class GO1Client: def __init__(self, host: str, port: int): self.host = host self.port = port def predict_action(self, payload: Dict[str, Any]) -> np.ndarray: response = requests.post( f"http://{self.host}:{self.port}/act", json=payload, headers={"Content-Type": "application/json"} ) if response.status_code == 200: result = response.json() action = np.array(result) return action else: print(f"Request failed, status code: {response.status_code}") print(f"Error message: {response.text}") return None ``` We can then run the LIBERO evaluation script to query the server, see the [LIBERO README](evaluate/libero/README.md) for details. #### More Examples We will provide more examples of fine-tuning and running inference with GO-1 models on real robots and simulation platforms. Currently we have: - [Genie Studio](https://genie.agibot.com/geniestudio): AgiBot G1 with out-of-the-box GO-1 model plus integrated data collection, fine-tuning, and deployment pipeline. - [AgileX](evaluate/agilex/README.md): AgileX Cobot Magic (Aloha) - [LIBERO](evaluate/libero/README.md): LIBERO Simulation (Franka) - [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin/tree/main/policy/GO1): RoboTwin Simulation (Aloha) ## 📄 License and Citation All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/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](CONTRIBUTING.md). - In alphabetical order by surname: ```BibTeX @article{bu2025agibot_arxiv, 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} } @inproceedings{bu2025agibot_iros, 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 He, Xindong and Huang, Xu and others}, booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year={2025}, organization={IEEE} } @article{shi2025diversity, title={Is Diversity All You Need for Scalable Robotic Manipulation?}, author={Shi, Modi and Chen, Li and Chen, Jin and Lu, Yuxiang and Liu, Chiming and Ren, Guanghui and Luo, Ping and Huang, Di and Yao, Maoqing and Li, Hongyang}, journal={arXiv preprint arXiv:2507.06219}, year={2025} } ``` ## 📝 Blogs ```BibTeX @misc{AgiBotWorldTeam2025agibot-world-colosseo, title = {Introducing AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems}, author = {Shi, Modi and Lu, Yuxiang and Wang, Huijie and Xie, Chengen and Bu, Qingwen}, year = {2025}, month = {March}, howpublished = {\url{https://opendrivelab.com/AgiBot-World/}}, note = {Blog post}, } @misc{AgiBotWorldTeam2025open-sourcing-go1, title = {Open-sourcing GO-1: The Bitter Lessons of Building VLA Systems at Scale}, author = {Shi, Modi and Lu, Yuxiang and Wang, Huijie and Yang, Shaoze}, year = {2025}, month = {September}, howpublished = {\url{https://opendrivelab.com/OpenGO1/}}, note = {Blog post}, } ```