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A modular high-level library to train embodied AI agents across a variety of tasks and environments.

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> [!WARNING]
> **Beyond v0.3.4 this project is no longer receiving official active development or maintenance by Meta internal teams. Please feel free to continue forking and developing the software independently.**

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Habitat-Lab
==============================

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

Towards this goal, Habitat-Lab is designed to support the following features:

- **Flexible task definitions**: allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks.
- **Diverse embodied agents**: configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities.
- **Training and evaluating agents**: providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics.
- **Human in the loop interaction**: providing a framework for humans to interact with the simulator, enabling to collect embodied data or interact with trained agents.

Habitat-Lab uses [`Habitat-Sim`](https://github.com/facebookresearch/habitat-sim) as the core simulator. For documentation refer [here](https://aihabitat.org/docs/habitat-lab/).

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</p>

---

## Table of contents
- [Habitat-Lab](#habitat-lab)
  - [Table of contents](#table-of-contents)
  - [Citing Habitat](#citing-habitat)
  - [Installation](#installation)
  - [Testing](#testing)
  - [Debugging an environment issue](#debugging-an-environment-issue)
  - [Documentation](#documentation)
  - [Docker Setup](#docker-setup)
    - [Questions?](#questions)
  - [Datasets](#datasets)
  - [Baselines](#baselines)
  - [ROS-X-Habitat](#ros-x-habitat)
  - [License](#license)

## Citing Habitat
If you use the Habitat platform in your research, please cite the [Habitat 1.0](https://arxiv.org/abs/1904.01201), [Habitat 2.0](https://arxiv.org/abs/2106.14405), and [Habitat 3.0](https://arxiv.org/abs/2310.13724) papers:

```
@misc{puig2023habitat3,
      title  = {Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots},
      author = {Xavi Puig and Eric Undersander and Andrew Szot and Mikael Dallaire Cote and Ruslan Partsey and Jimmy Yang and Ruta Desai and Alexander William Clegg and Michal Hlavac and Tiffany Min and Theo Gervet and Vladimír Vondruš and Vincent-Pierre Berges and John Turner and Oleksandr Maksymets and Zsolt Kira and Mrinal Kalakrishnan and Jitendra Malik and Devendra Singh Chaplot and Unnat Jain and Dhruv Batra and Akshara Rai and Roozbeh Mottaghi},
      year={2023},
      archivePrefix={arXiv},
}

@inproceedings{szot2021habitat,
  title     =     {Habitat 2.0: Training Home Assistants to Rearrange their Habitat},
  author    =     {Andrew Szot and Alex Clegg and Eric Undersander and Erik Wijmans and Yili Zhao and John Turner and Noah Maestre and Mustafa Mukadam and Devendra Chaplot and Oleksandr Maksymets and Aaron Gokaslan and Vladimir Vondrus and Sameer Dharur and Franziska Meier and Wojciech Galuba and Angel Chang and Zsolt Kira and Vladlen Koltun and Jitendra Malik and Manolis Savva and Dhruv Batra},
  booktitle =     {Advances in Neural Information Processing Systems (NeurIPS)},
  year      =     {2021}
}

@inproceedings{habitat19iccv,
  title     =     {Habitat: {A} {P}latform for {E}mbodied {AI} {R}esearch},
  author    =     {Manolis Savva and Abhishek Kadian and Oleksandr Maksymets and Yili Zhao and Erik Wijmans and Bhavana Jain and Julian Straub and Jia Liu and Vladlen Koltun and Jitendra Malik and Devi Parikh and Dhruv Batra},
  booktitle =     {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      =     {2019}
}

```

## Installation

1. **Preparing conda env**

Assuming you have [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) installed, let's prepare a conda env:
   ```bash
   # We require python>=3.9 and cmake>=3.14
   conda create -n habitat python=3.9 cmake=3.14.0
   conda activate habitat
   ```

1. **conda install habitat-sim**
   - To install habitat-sim with bullet physics
      ```
      conda install habitat-sim withbullet -c conda-forge -c aihabitat
      ```
      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.

1. **pip install habitat-lab stable version**.

```bash
      git clone --branch stable https://github.com/facebookresearch/habitat-lab.git
      cd habitat-lab
      pip install -e habitat-lab  # install habitat_lab
      ```
1. **Install habitat-baselines**.

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:

```bash
      pip install -e habitat-baselines  # install habitat_baselines
      ```

## Testing

1. Let's download some 3D assets using Habitat-Sim's python data download utility:
   - Download (testing) 3D scenes:
      ```bash
      python -m habitat_sim.utils.datasets_download --uids habitat_test_scenes --data-path data/
      ```
      Note that these testing scenes do not provide semantic annotations.

- Download point-goal navigation episodes for the test scenes:
      ```bash
      python -m habitat_sim.utils.datasets_download --uids habitat_test_pointnav_dataset --data-path data/
      ```

1. **Non-interactive testing**: Test the Pick task: Run the example pick task script
    
    ```bash
    python examples/example.py
    ```

which uses [`habitat-lab/habitat/config/benchmark/rearrange/skills/pick.yaml`](habitat-lab/habitat/config/benchmark/rearrange/skills/pick.yaml) for configuration of task and agent. The script roughly does this:

```python
    import gym
    import habitat.gym

# Load embodied AI task (RearrangePick) and a pre-specified virtual robot
    env = gym.make("HabitatRenderPick-v0")
    observations = env.reset()

terminal = False

# Step through environment with random actions
    while not terminal:
        observations, reward, terminal, info = env.step(env.action_space.sample())
    ```

To modify some of the configurations of the environment, you can also use the `habitat.gym.make_gym_from_config` method that allows you to create a habitat environment using a configuration.

```python
    config = habitat.get_config(
      "benchmark/rearrange/skills/pick.yaml",
      overrides=["habitat.environment.max_episode_steps=20"]
    )
    env = habitat.gym.make_gym_from_config(config)
    ```

If you want to know more about what the different configuration keys overrides do, you can use [this reference](habitat-lab/habitat/config/CONFIG_KEYS.md).

See [`examples/register_new_sensors_and_measures.py`](examples/register_new_sensors_and_measures.py) for an example of how to extend habitat-lab from _outside_ the source code.

1. **Interactive testing**: Using you keyboard and mouse to control a Fetch robot in a ReplicaCAD environment:
    ```bash
    # Pygame for interactive visualization, pybullet for inverse kinematics
    pip install pygame==2.0.1 pybullet==3.0.4

# Interactive play script
    python examples/interactive_play.py --never-end
    ```

Use I/J/K/L keys to move the robot base forward/left/backward/right and W/A/S/D to move the arm end-effector forward/left/backward/right and E/Q to move the arm up/down. The arm can be difficult to control via end-effector control. More details in documentation. Try to move the base and the arm to touch the red bowl on the table. Have fun!

Note: Interactive testing currently fails on Ubuntu 20.04 with an error: `X Error of failed request:  BadAccess (attempt to access private resource denied)`. We are working on fixing this, and will update instructions once we have a fix. The script works without errors on MacOS.

## Debugging an environment issue

Our vectorized environments are very fast, but they are not very verbose. When using `VectorEnv` some errors may be silenced, resulting in process hanging or multiprocessing errors that are hard to interpret. We recommend setting the environment variable `HABITAT_ENV_DEBUG` to 1 when debugging (`export HABITAT_ENV_DEBUG=1`) as this will use the slower, but more verbose `ThreadedVectorEnv` class. Do not forget to reset `HABITAT_ENV_DEBUG` (`unset HABITAT_ENV_DEBUG`) when you are done debugging since `VectorEnv` is much faster than `ThreadedVectorEnv`.

## Documentation

Browse the online [Habitat-Lab documentation](https://aihabitat.org/docs/habitat-lab/index.html) and the extensive [tutorial on how to train your agents with Habitat](https://aihabitat.org/tutorial/2020/). For Habitat 2.0, use this [quickstart guide](https://aihabitat.org/docs/habitat2/).

## Docker Setup
We 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:

1. Pull the habitat docker image: `docker pull fairembodied/habitat-challenge:testing_2022_habitat_base_docker`

1. Start an interactive bash session inside the habitat docker: `docker run --runtime=nvidia -it fairembodied/habitat-challenge:testing_2022_habitat_base_docker`

1. Activate the habitat conda environment: `conda init; source ~/.bashrc; source activate habitat`

1. Run the testing scripts as above: `cd habitat-lab; python examples/example.py`. This should print out an output like:
    ```bash
    Agent acting inside environment.
    Episode finished after 200 steps.
    ```

### Questions?
Can't find the answer to your question? Look up for [common issues](./TROUBLESHOOTING.md) or try asking the developers and community on our [Discussions forum](https://github.com/facebookresearch/habitat-lab/discussions).

## Datasets

[Common task and episode datasets used with Habitat-Lab](DATASETS.md).

## Baselines
Habitat-Lab includes reinforcement learning (via PPO) baselines. For running PPO training on sample data and more details refer [habitat_baselines/README.md](habitat-baselines/habitat_baselines/README.md).

## ROS-X-Habitat
ROS-X-Habitat (https://github.com/ericchen321/ros_x_habitat) is a framework that bridges the AI Habitat platform (Habitat Lab + Habitat Sim) with other robotics resources via ROS. ROS-X-Habitat places emphasis on 1) leveraging Habitat Sim v2's physics-based simulation capability and 2) allowing roboticists to access simulation assets from ROS. The work has also been made public as a [paper](https://arxiv.org/abs/2109.07703).

Note that ROS-X-Habitat was developed, and is maintained by the Lab for Computational Intelligence at UBC; it has not yet been officially supported by the Habitat Lab team. Please refer to the framework's repository for docs and discussions.

## License
Habitat-Lab is MIT licensed. See the [LICENSE file](/LICENSE) for details.

The trained models and the task datasets are considered data derived from the correspondent scene datasets.

- 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/).
- 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/).

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