{"data":{"slug":"facebookresearch-habitat-lab","name":"habitat-lab","tagline":"A modular high-level library to train embodied AI agents","github_url":"https://github.com/facebookresearch/habitat-lab","owner":"facebookresearch","repo":"habitat-lab","owner_avatar_url":"https://avatars.githubusercontent.com/u/16943930?v=4","primary_language":"Python","stars":3082,"forks":684,"topics":["ai","computer-vision","deep-learning","deep-reinforcement-learning","python","reinforcement-learning","research","robotics","sim2real","simulator"],"archived":false,"github_pushed_at":"2026-05-07T22:03:51+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/facebookresearch-habitat-lab","markdown_url":"https://www.graphcanon.com/tools/facebookresearch-habitat-lab.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/facebookresearch-habitat-lab","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=facebookresearch-habitat-lab","description":"A modular high-level library to train embodied AI agents across a variety of tasks and environments.","homepage_url":"https://aihabitat.org/","license":"MIT","open_issues":388,"watchers":43,"ai_summary":"Habitat-Lab provides tools for training AI in virtual environments using deep learning and reinforcement learning techniques, targeting tasks related to robotics and computer vision.","readme_excerpt":"## Installation\n\n1. **Preparing conda env**\n\n   Assuming you have [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) installed, let's prepare a conda env:\n   ```bash\n   # We require python>=3.9 and cmake>=3.14\n   conda create -n habitat python=3.9 cmake=3.14.0\n   conda activate habitat\n   ```\n\n1. **conda install habitat-sim**\n   - To install habitat-sim with bullet physics\n      ```\n      conda install habitat-sim withbullet -c conda-forge -c aihabitat\n      ```\n      Note, for newer features added after the most recent release, you may need to install `aihabitat-nightly`. See Habitat-Sim's [installation instructions](https://github.com/facebookresearch/habitat-sim#installation) for more details.\n\n1. **pip install habitat-lab stable version**.\n\n      ```bash\n      git clone --branch stable https://github.com/facebookresearch/habitat-lab.git\n      cd habitat-lab\n      pip install -e habitat-lab  # install habitat_lab\n      ```\n1. **Install habitat-baselines**.\n\n    The command above will install only core of Habitat-Lab. To include habitat_baselines along with all additional requirements, use the command below after installing habitat-lab:\n\n      ```bash\n      pip install -e habitat-baselines  # install habitat_baselines\n      ```\n\n---\n\n## Docker Setup\nWe provide docker containers for Habitat, updated approximately once per year for the [Habitat Challenge](https://github.com/facebookresearch/habitat-challenge). This works on machines with an NVIDIA GPU and requires users to install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). To setup the habitat stack using docker follow the below steps:\n\n1. Pull the habitat docker image: `docker pull fairembodied/habitat-challenge:testing_2022_habitat_base_docker`\n\n1. Start an interactive bash session inside the habitat docker: `docker run --runtime=nvidia -it fairembodied/habitat-challenge:testing_2022_habitat_base_docker`\n\n1. Activate the habitat conda environment: `conda init; source ~/.bashrc; source activate habitat`\n\n1. Run the testing scripts as above: `cd habitat-lab; python examples/example.py`. This should print out an output like:\n    ```bash\n    Agent acting inside environment.\n    Episode finished after 200 steps.\n    ```\n\n---\n\n## License\nHabitat-Lab is MIT licensed. See the [LICENSE file](/LICENSE) for details.\n\nCopyright (c) Meta Platforms, Inc. and affiliates.\n\nThe trained models and the task datasets are considered data derived from the correspondent scene datasets.\n\n- Matterport3D based task datasets and trained models are distributed with [Matterport3D Terms of Use](http://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf) and under [CC BY-NC-SA 3.0 US license](https://creativecommons.org/licenses/by-nc-sa/3.0/us/).\n- Gibson based task datasets, the code for generating such datasets, and trained models are distributed with [Gibson Terms of Use](https://storage.googleapis.com/gibson_material/Agreement%20GDS%2006-04-18.pdf) and under [CC BY-NC-SA 3.0 US license](https://creativecommons.org/licenses/by-nc-sa/3.0/us/).","github_created_at":"2019-02-04T23:12:51+00:00","created_at":"2026-07-11T12:23:09.407489+00:00","updated_at":"2026-07-31T06:00:34.370659+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"reinforcement-learning","name":"reinforcement-learning"},{"slug":"research","name":"research"},{"slug":"robotics","name":"robotics"},{"slug":"sim2real","name":"sim2real"},{"slug":"simulator","name":"simulator"}],"trust":{"provenance":{"is_fork":false,"github_id":169164391,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-31T06:00:33.344Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":1,"days_since_push":84,"last_release_at":"2026-05-07T20:22:56Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:23:11.303Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-31T06:00:33.822Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-07-31T06:00:33.822Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-31T06:00:33.822Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-07-31T06:00:33.822Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-31T06:00:33.822Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Python >=3.9 is required along with cmake>=3.14 for installation","For users working on machines equipped with NVIDIA GPUs, nvidia-docker installation is necessary to run the provided Docker containers"],"min_ram_gb":8,"requires_docker":true},"constraints":{"min_ram_gb":8,"requires_docker":true},"when_to_use":["Use Habitat-Lab when your project requires the simulation of complex environments for embodied AI tasks, such as navigation and interaction with objects","If you are working on research projects involving robotics and computer vision where simulation is critical to test and validate your algorithms before physical trials, choose Habitat-Lab"],"when_not_to_use":["Avoid Habitat-Lab if the computational resources required for running the simulations exceed what is available or feasible in terms of cost","Do not use Habitat-Lab when the project is solely focused on real-world data and does not necessitate virtual training environments, as setting up such a library might add unnecessary complexity"],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:29:38.030Z"},"constraint_facets":{"min_ram_gb":8,"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Min 8 GB RAM; Requires Docker; Python >=3.9 is required along with cmake>=3.14 for installation; For users working on machines equipped with NVIDIA GPUs, nvidia-docker installation is necessary to run the provided Docker containers"},{"label":"Adopt for","value":"Habitat-Lab is a Python library for training embodied AI agents in virtual environments through deep and reinforcement learning techniques."}]}}