Welcome to the exciting world of Habitat-Lab. This modular, high-level library is your gateway to advancing embodied AI through training agents for various tasks in indoor environments. In this blog, we will explore the steps to install and use Habitat-Lab, troubleshoot common issues, and provide an easy-to-understand analogy to help you better comprehend the code. Let’s jump in!

What is Habitat-Lab?

Habitat-Lab is designed for end-to-end development in embodied AI, allowing users to train agents on tasks such as navigation, rearrangement, and even human interaction. It supports diverse task definitions and agent configurations, making it a versatile tool for researchers and developers.

Installation Steps

Here’s how you can install Habitat-Lab on your system:

1. Prepare your conda environment:

   conda create -n habitat python=3.9 cmake=3.14.0

   Activate the environment:

   conda activate habitat

2. Install Habitat-Sim:

   conda install habitat-sim withbullet -c conda-forge -c aihabitat

3. Clone and install Habitat-Lab:

   git clone --branch stable https://github.com/facebookresearch/habitat-lab.git

   cd habitat-lab

   pip install -e habitat-lab

4. Install Habitat-Baselines:

   pip install -e habitat-baselines

Testing Your Installation

After installation, you can test if everything is working correctly:

1. Download 3D assets:

   python -m habitat_sim.utils.datasets_download --uids habitat_test_scenes --data-path data

2. Run a sample script:

   python examples/example.py

Understanding the Code: An Analogy

Imagine you’re setting up a board game that involves multiple players (agents) competing to achieve different objectives (tasks) in a shared environment (the board). In order to start playing, each player needs their pieces configured correctly (agent configuration), and the rules of the game need to be defined (task definitions). Just like in the code:

import gym
import habitat.gym

env = gym.make('HabitatRenderPick-v0')
observations = env.reset()
terminal = False

while not terminal:
    observations, reward, terminal, info = env.step(env.action_space.sample())

In this analogy, the players use random moves on the board until one achieves their task (the ‘Pick’ task), just as the code continues to make moves in the environment until the condition for completion (a terminal state) is met.

Troubleshooting

If you encounter issues while setting up or using Habitat-Lab, consider the following troubleshooting tips:

• Check your conda environment to ensure it is activated correctly.
• Make sure that all installation commands completed successfully without errors.
• Setting the environment variable HABITAT_ENV_DEBUG to 1 offers more verbose debugging outputs, which can help point out errors:

export HABITAT_ENV_DEBUG=1

• Once you’re finished debugging, reset the variable with:

  unset HABITAT_ENV_DEBUG

• If you still have questions, refer to the Discussions forum or check for common issues.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these steps, you can effectively set up Habitat-Lab and begin exploring the phenomenal world of embodied AI. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.