{"id":45772181,"url":"https://github.com/OpenDriveLab/AgiBot-World","last_synced_at":"2026-03-11T15:01:07.394Z","repository":{"id":270287995,"uuid":"900546855","full_name":"OpenDriveLab/AgiBot-World","owner":"OpenDriveLab","description":"[IROS 2025 Best Paper Award Finalist \u0026 IEEE TRO 2026] The Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems","archived":false,"fork":false,"pushed_at":"2025-12-16T05:47:46.000Z","size":49645,"stargazers_count":2798,"open_issues_count":30,"forks_count":196,"subscribers_count":35,"default_branch":"main","last_synced_at":"2026-03-01T08:39:32.328Z","etag":null,"topics":["pretraining-for-robotics","robotic-foundation-model","robotic-manipulation","vision-language-action-model"],"latest_commit_sha":null,"homepage":"https://opendrivelab.com/AgiBot-World/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OpenDriveLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null},"funding":{"github":["OpenDriveLab"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2024-12-09T02:35:58.000Z","updated_at":"2026-03-01T00:44:19.000Z","dependencies_parsed_at":"2025-07-12T10:05:51.517Z","dependency_job_id":"34590bd5-b828-40ed-b3c9-09e7bf91b8b5","html_url":"https://github.com/OpenDriveLab/AgiBot-World","commit_stats":null,"previous_names":["opendrivelab/agibot-world"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/OpenDriveLab/AgiBot-World","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FAgiBot-World","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FAgiBot-World/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FAgiBot-World/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FAgiBot-World/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OpenDriveLab","download_url":"https://codeload.github.com/OpenDriveLab/AgiBot-World/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OpenDriveLab%2FAgiBot-World/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30385018,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-11T14:10:17.325Z","status":"ssl_error","status_checked_at":"2026-03-11T14:09:37.934Z","response_time":84,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["pretraining-for-robotics","robotic-foundation-model","robotic-manipulation","vision-language-action-model"],"created_at":"2026-02-26T07:00:20.876Z","updated_at":"2026-03-11T15:01:07.388Z","avatar_url":"https://github.com/OpenDriveLab.png","language":"Python","funding_links":["https://github.com/sponsors/OpenDriveLab"],"categories":["Python","1. 机器人项目 | Robots"],"sub_categories":[],"readme":"\u003cdiv id=\"top\" align=\"center\"\u003e\n\n![agibot_world](https://github.com/user-attachments/assets/df64b543-db82-41ee-adda-799970e8a198)\n\n\u003ca href=\"https://opendrivelab.com/OpenGO1/\" target=\"_blank\"\u003eResearch Blog: GO-1 Open-sourcing\u003c/a\u003e | \u003ca href=\"https://opendrivelab.com/blog/agibot-world/\" target=\"_blank\"\u003eResearch Blog: AgiBot World Colosseo\u003c/a\u003e | \u003ca href=\"https://arxiv.org/abs/2503.06669\" target=\"_blank\"\u003eTechnical Report\u003c/a\u003e\n\n\u003ca href=\"https://arxiv.org/abs/2503.06669\"\u003e\u003cimg src=\"https://img.shields.io/badge/arXiv-Paper-\u003ccolor\u003e\"\u003e\u003c/a\u003e [![Static Badge](https://img.shields.io/badge/Dataset-grey?style=plastic\u0026logo=huggingface\u0026logoColor=yellow)](https://huggingface.co/agibot-world) [![Static Badge](https://img.shields.io/badge/Project%20Page-blue?style=plastic)](https://agibot-world.com) [![License](https://img.shields.io/badge/License-CC_%20_BY--NC--SA_4.0-blue.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n\u003ca href=\"https://docs.google.com/spreadsheets/d/1GWMFHYo3UJADS7kkScoJ5ObbQfAFasPuaeC7TJUr1Cc/edit?usp=sharing\"\u003e\u003cimg src=\"https://img.shields.io/badge/Dataset-Overview-brightgreen?logo=googleforms\" alt=\"Document Badge\"\u003e\u003c/a\u003e [![Static Badge](https://img.shields.io/badge/Model-grey?style=plastic\u0026logo=huggingface\u0026logoColor=yellow)](https://huggingface.co/agibot-world/GO-1)\n\n\u003c/div\u003e\n\nAgiBot 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.\n\nWe have released:\n- **\u003ca href=\"https://huggingface.co/agibot-world/GO-1\" target=\"_blank\"\u003eGO-1\u003c/a\u003e:** Our robotic foundation model pretrained on AgiBot World Dataset\n- **\u003ca href=\"https://huggingface.co/agibot-world/GO-1-Air\" target=\"_blank\"\u003eGO-1 Air\u003c/a\u003e:** GO-1 model without Latent Planner, high-performanced and lightweighted\n- **\u003ca href=\"https://docs.google.com/spreadsheets/d/1GWMFHYo3UJADS7kkScoJ5ObbQfAFasPuaeC7TJUr1Cc/edit?usp=sharing\" target=\"_blank\"\u003eTask Catalog\u003c/a\u003e:** Reference sheet outlining the tasks in our dataset, including robot end-effector types, sample action-text descriptions and more\n- **\u003ca href=\"https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta\" target=\"_blank\"\u003eAgiBot World Beta\u003c/a\u003e:** Our complete dataset featuring 1,003,672 trajectories (~43.8T)\n- **\u003ca href=\"https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha\" target=\"_blank\"\u003eAgiBot World Alpha\u003c/a\u003e:** A curated subset of AgiBot World Beta, containing 92,214 trajectories (~8.5T)\n\n## News📰 \u003ca name=\"news\"\u003e\u003c/a\u003e\n\n\u003e [!IMPORTANT]\n\u003e 🌟 Stay up to date at [opendrivelab.com](https://opendrivelab.com/#news)!\n\n- **`[2025/09/19]`** 🚀 **Our robotic foundation model GO-1 open-sourced.**\n- **`[2025/03/10]`** 📄 \u003ca href=\"https://opendrivelab.com/blog/agibot-world/\" target=\"_blank\"\u003eResearch Blog\u003c/a\u003e and \u003ca href=\"https://arxiv.org/abs/2503.06669\" target=\"_blank\"\u003eTechnical Report\u003c/a\u003e released.\n- **`[2025/03/01]`** Agibot World Beta released.\n- **`[2025/01/03]`** \u003cspan style=\"color: #B91C1C; font-weight: bold;\"\u003eAgibot World Alpha Sample Dataset released.\u003c/span\u003e\n- **`[2024/12/30]`** 🤖 Agibot World Alpha released.\n\n## TODO List 📅 \u003ca name=\"todolist\"\u003e\u003c/a\u003e\n\n- [x] **AgiBot World Alpha**\n- [x] **AgiBot World Beta**\n  - [x] ~1,000,000 trajectories of high-quality robot data \n- [x] **AgiBot World Foundation Model: GO-1**\n  - [x] GO-1 fine-tuning script\n  - [x] GO-1 Air pre-trained checkpoint\n  - [x] GO-1 pre-trained checkpoint\n  - [x] Examples of using GO-1 model\n- [x] **2025 AgiBot World Challenge**\n\n## Key Features 🔑 \u003ca name=\"keyfeatures\"\u003e\u003c/a\u003e\n\n- **1 million+** trajectories from 100 robots.\n- **100+ 1:1 replicated real-life scenarios** across 5 target domains.\n- **Cutting-edge hardware:** visual tactile sensors / 6-DoF Dexterous hand / mobile dual-arm robots\n- **Wide-spectrum versatile challenging tasks**\n- **General robotic policy pretrained on AgiBot World**\n\n\u003cdiv style=\"max-width: 100%; overflow-x: auto; margin: 0 auto; !important;\"\u003e\n  \u003ctable style=\"border-collapse: collapse; border-spacing: 0; width: 100%; table-layout: fixed;\"\u003e\n    \u003ctr style=\"border: none;\"\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/Contact-rich_manipulation.gif\" alt=\"Contact-rich Manipulation\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eContact-rich Manipulation\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/Long-horizon_planning.gif\" alt=\"Long-horizon Planning\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eLong-horizon Planning\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/Multi-robot_collaboration.gif\" alt=\"Multi-robot Collaboration\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eMulti-robot Collaboration\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr style=\"border: none;\"\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/agilex_fold_shirts.gif\" alt=\"Fold Shirt (AgileX)\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eFold Shirt (AgileX)\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/g1_fold_shirts.gif\" alt=\"Fold Shirt (AgiBot G1)\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eFold Shirt (AgiBot G1)\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n      \u003ctd align=\"center\" style=\"border: none; padding: 10px;\"\u003e\n        \u003cimg src=\"assets/franka_fold_shirts.gif\" alt=\"Fold Shirt (Dual Franka)\" width=\"230\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\"\u003e\n        \u003cp\u003e\u003cb\u003eFold Shirt (Dual Franka)\u003c/b\u003e\u003c/p\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/table\u003e\n\u003c/div\u003e\n\n## Table of Contents\n\n- [News📰 ](#news-)\n- [TODO List 📅 ](#todo-list--)\n- [Key Features 🔑 ](#key-features--)\n- [Table of Contents](#table-of-contents)\n- [Getting started 🔥 ](#getting-started--)\n  - [Installation ](#installation-)\n  - [How to Get Started with Our AgiBot World Data ](#how-to-get-started-with-our-agibot-world-data-)\n    - [Download Datasets ](#download-datasets-)\n    - [Visualize Datasets ](#visualize-datasets-)\n  - [How to Get Started with Our GO-1 Model ](#how-to-get-started-with-our-go-1-model-)\n    - [Requirements ](#requirements-)\n    - [Model Zoo ](#model-zoo-)\n    - [Fine-tuning on Your Own Dataset ](#fine-tuning-on-your-own-dataset-)\n    - [Testing Your Model ](#testing-your-model-)\n    - [More Examples ](#more-examples-)\n- [License and Citation📄   ](#license-and-citation---)\n\n## Getting started 🔥 \u003ca name=\"gettingstarted\"\u003e\u003c/a\u003e\n\n### Installation \u003ca name=\"installation\"\u003e\u003c/a\u003e\n\n1. Download our source code:\n```bash\ngit clone https://github.com/OpenDriveLab/AgiBot-World.git\ncd AgiBot-World\n```\n\n2. Create a new conda environment:\n```bash\nconda create -n go1 python=3.10 -y\nconda activate go1\n```\n\n3. Install dependencies:\n\u003e This project is built on [LeRobot](https://github.com/huggingface/lerobot) (**dataset `v2.1`, commit `2b71789`**)  \n\u003e ⚡️ Our environment has been tested with **CUDA 12.4**.\n```bash\npip install -e .\npip install --no-build-isolation flash-attn==2.4.2\n```\n\nIf 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:\n```bash\nMAX_JOBS=4 pip install --no-build-isolation flash-attn==2.4.2\n```\n\n### How to Get Started with Our AgiBot World Data \u003ca name=\"startdata\"\u003e\u003c/a\u003e\n\n#### Download Datasets \u003ca name=\"downloaddatasets\"\u003e\u003c/a\u003e\n\n- [OPTION 1] Download data from our [OpenDataLab](https://opendatalab.com/OpenDriveLab/AgiBot-World) page.\n\n```bash\npip install openxlab # install CLI\nopenxlab dataset get --dataset-repo OpenDriveLab/AgiBot-World # dataset download\n```\n\n- [OPTION 2] Download data from our [HuggingFace](https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha) page.\n\n```bash\nhuggingface-cli download --resume-download --repo-type dataset agibot-world/AgiBotWorld-Alpha --local-dir ./AgiBotWorld-Alpha\n```\n\nConvert the data to **LeRobot Dataset** format following [any4lerobot](https://github.com/Tavish9/any4lerobot).\n\n#### Visualize Datasets \u003ca name=\"visualizedatasets\"\u003e\u003c/a\u003e\n\nWe adapt and extend the dataset visualization script from [LeRobot Project](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/visualize_dataset.py):\n\n```bash\npython scripts/visualize_dataset.py --task-id 390 --dataset-path /path/to/lerobot/format/dataset\n```\n\nIt will open `rerun.io` and display the camera streams, robot states and actions, like this:\n\u003cdiv style=\"text-align: center;\"\u003e\n\u003cimg src=\"assets/dataset_visualization.gif\" width=\"600\"\u003e\n\u003c/div\u003e\n\n### How to Get Started with Our GO-1 Model \u003ca name=\"startmodel\"\u003e\u003c/a\u003e\n\n#### Requirements \u003ca name=\"requirements\"\u003e\u003c/a\u003e\n\nWe strongly recommend full fine-tuning for the best performance. However, if GPU memory is limited, you can alternatively fine-tune only the Action Expert.\n\n|         Usage         |  GPU Memory Required  |     Example GPU     |\n| :-------------------: | :-------------------: | :-----------------: |\n|       Inference       |         ~7GB          |      RTX 4090       |\n|  Fine-tuning (Full)   | ~70GB (batch size=16) |   A100 80GB, H100   |\n| Fine-tuning (Only AE) | ~24GB (batch size=16) | RTX 4090, A100 40GB |\n\n#### Model Zoo \u003ca name=\"modelzoo\"\u003e\u003c/a\u003e\n\n|  Model   |                   HF Link                    |                              Description                              |\n| :------: | :------------------------------------------: | :-------------------------------------------------------------------: |\n| GO-1 Air | https://huggingface.co/agibot-world/GO-1-Air | GO-1 model without Latent Planner pre-trained on AgiBot World dataset |\n|   GO-1   |   https://huggingface.co/agibot-world/GO-1   |            GO-1 model pre-trained on AgiBot World dataset             |\n\n#### Fine-tuning on Your Own Dataset \u003ca name=\"finetune\"\u003e\u003c/a\u003e\n\nHere 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.\n\n**1. Prepare Data**\n\nWe 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).\n\nSince 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:\n\n```bash\nconda create -n libero_data python=3.10 -y\nconda activate libero_data\n\npip install -e \".[libero_data]\"\n```\n\nDownload the raw LIBERO dataset from [OpenVLA](https://huggingface.co/datasets/openvla/modified_libero_rlds), then run the script to convert it into LeRobot dataset:\n\n```bash\n# Optional: Change the LeRobot home directory\nexport HF_LEROBOT_HOME=/path/to/your/lerobot\n\npython evaluate/libero/convert_libero_data_to_lerobot.py --data_dir /path/to/your/libero/data\n```\n\n**2. Prepare Configs**\n\nWe provide an example config for fine-tuning GO-1 on LIBERO in [go1/configs/go1_sft_libero.py](go1/configs/go1_sft_libero.py).\n\nKey sections in the config:\n- `DatasetArguments` - path or repo for the LeRobot dataset.\n- `GOModelArguments` - model settings: architecture (GO-1 Air or GO-1), action chunk size, diffusion scheduler, parameter freezing, etc.\n- `GOTrainingArguments` - training hyper-parameters, see [transformers docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) for more details.\n- `SpaceArguments` - state/action dimensions, data keys in the LeRobot dataset, default language prompt, control frequency.\n\nSee [go1/configs/go1_base_cfg.py](go1/configs/go1_base_cfg.py) for all available config options.\n\n**3. Start Fine-tuning**\n\nStart fine-tuning with the following command, you can setup environment variables according to the [shell](go1/shell/train.sh).\n\n```bash\nRUNNAME=\u003cYOUR_RUNNAME\u003e bash go1/shell/train.sh /path/to/your/config\n```\n\nCheckpoints will be saved in `experiment/\u003cYOUR_RUNNAME\u003e` and logs will be saved in `experiment/\u003cYOUR_RUNNAME\u003e/logs`.\n\n**Notes:**\n- 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. \n- 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/\u003cYOUR_RUNNAME\u003e/dataset_stats.json`.\n- 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.\n\n\n#### Testing Your Model \u003ca name=\"inference\"\u003e\u003c/a\u003e\n\n**Local Inference**\n\nAfter 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:\n\n```python\nimport numpy as np\nfrom evaluate.deploy import GO1Infer\n\nmodel = GO1Infer(model_path=\"/path/to/your/checkpoint\", data_stats_path=\"/path/to/your/dataset_stats.json\")\n\npayload = {\n    \"top\": ...,\n    \"right\": ...,\n    \"left\": ...,\n    \"instruction\": \"example instruction\",\n    \"state\": ...,\n    \"ctrl_freqs\": np.array([30]),\n}\n\nactions = model.inference(payload)\n```\n\nWe also provide a script for open-loop evaluation with training data in [evaluate/openloop_eval.py](evaluate/openloop_eval.py).\n\n**Remote Inference**\n\nConsidering 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.\n\nStart the server and it will listen on port `PORT` and waits for observations:\n\n```bash\npython evaluate/deploy.py --model_path /path/to/your/checkpoint --data_stats_path /path/to/your/dataset_stats.json --port \u003cPORT\u003e\n```\n\nFor the client, we provide a `GO1Client` class to send requests to the server and receive actions:\n\n```python\nfrom typing import Dict, Any\n\nimport json_numpy\nimport numpy as np\nimport requests\n\njson_numpy.patch()\n\nclass GO1Client:\n  def __init__(self, host: str, port: int):\n      self.host = host\n      self.port = port\n\n  def predict_action(self, payload: Dict[str, Any]) -\u003e np.ndarray:\n      response = requests.post(\n          f\"http://{self.host}:{self.port}/act\", json=payload, headers={\"Content-Type\": \"application/json\"}\n      )\n\n      if response.status_code == 200:\n          result = response.json()\n          action = np.array(result)\n          return action\n      else:\n          print(f\"Request failed, status code: {response.status_code}\")\n          print(f\"Error message: {response.text}\")\n          return None\n```\n\nWe can then run the LIBERO evaluation script to query the server, see the [LIBERO README](evaluate/libero/README.md) for details.\n\n\n#### More Examples \u003ca name=\"examples\"\u003e\u003c/a\u003e\nWe will provide more examples of fine-tuning and running inference with GO-1 models on real robots and simulation platforms.\n\nCurrently we have:\n- [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.\n- [AgileX](evaluate/agilex/README.md): AgileX Cobot Magic (Aloha)\n- [LIBERO](evaluate/libero/README.md): LIBERO Simulation (Franka)\n- [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin/tree/main/policy/GO1): RoboTwin Simulation (Aloha)\n\n\u003c!-- \u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e --\u003e\n\n\n\n\u003c!-- \u003cp align=\"right\"\u003e(\u003ca href=\"#top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e --\u003e\n\n\n\n## 📄 License and Citation   \u003ca name=\"liscenseandcitation\"\u003e\u003c/a\u003e\n\nAll the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). \n\n- Please consider citing our work if it helps your research.\n- For the full authorship and detailed contributions, please refer to [contributions](CONTRIBUTING.md).\n- In alphabetical order by surname:\n```BibTeX\n@article{bu2025agibot_arxiv,\n  title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},\n  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},\n  journal={arXiv preprint arXiv:2503.06669},\n  year={2025}\n}\n\n@inproceedings{bu2025agibot_iros,\n  title={Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems},\n  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},\n  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n  year={2025},\n  organization={IEEE}\n}\n\n@article{shi2025diversity,\n  title={Is Diversity All You Need for Scalable Robotic Manipulation?},\n  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},\n  journal={arXiv preprint arXiv:2507.06219},\n  year={2025}\n}\n```\n\n## 📝 Blogs  \u003ca name=\"blogs\"\u003e\u003c/a\u003e\n```BibTeX\n@misc{AgiBotWorldTeam2025agibot-world-colosseo,\n          title        = {Introducing AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems},\n          author       = {Shi, Modi and Lu, Yuxiang and Wang, Huijie and Xie, Chengen and Bu, Qingwen},\n          year         = {2025},\n          month        = {March},\n          howpublished = {\\url{https://opendrivelab.com/AgiBot-World/}},\n          note         = {Blog post},\n        }\n\n@misc{AgiBotWorldTeam2025open-sourcing-go1,\n          title        = {Open-sourcing GO-1: The Bitter Lessons of Building VLA Systems at Scale},\n          author       = {Shi, Modi and Lu, Yuxiang and Wang, Huijie and Yang, Shaoze},\n          year         = {2025},\n          month        = {September},\n          howpublished = {\\url{https://opendrivelab.com/OpenGO1/}},\n          note         = {Blog post},\n        }\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOpenDriveLab%2FAgiBot-World","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FOpenDriveLab%2FAgiBot-World","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOpenDriveLab%2FAgiBot-World/lists"}