Welcome to the exciting world of Vision-and-Language Navigation in Continuous Environments (VLN-CE)! In this guide, we’ll walk you through the essential steps to set up VLN-CE on your machine. By the end of this guide, you’ll be ready to harness the power of AI to navigate through realistic environments by following complex human instructions.

Understanding VLN-CE

VLN-CE is essentially like training a robot to follow clear and detailed directions in various environments. Imagine you are giving directions to a friend in a vast building — you want them to move through rooms and corridors based on specific instructions, like “turn left after the second door.” The goal of VLN-CE is to enable an AI agent to execute such tasks seamlessly through different environments.

Getting Started with Setup

Before jumping into the installation steps, ensure that you have Python 3.6 installed on your machine. If you’re already using [miniconda](https://docs.conda.io/en/latest/miniconda.html) or [anaconda](https://anaconda.org), follow these steps:

• Create a new conda environment:

conda create -n vlnce python=3.6

• Activate the environment:

conda activate vlnce

Installing Dependencies

VLN-CE relies on the Habitat platform for simulations. Here’s how you can install it:

• Install Habitat-Sim:

conda install -c aihabitat -c conda-forge habitat-sim=0.1.7 headless

• Clone and install Habitat-Lab:

git clone --branch v0.1.7 git@github.com:facebookresearch/habitat-lab.git

cd habitat-lab

python -m pip install -r requirements.txt

python setup.py develop --all

Installing VLN-CE

With Habitat installed, you can now proceed to set up VLN-CE:

• Clone the VLN-CE repository:

git clone git@github.com:jacobkrantz/VLN-CE.git

cd VLN-CE

• Install the required packages:

python -m pip install -r requirements.txt

Acquiring the Data

VLN-CE relies on datasets like Matterport3D for generating environments and Room-to-Room (R2R) instructions. Follow these steps to set up the data:

• Download Matterport3D scenes:

python download_mp.py --task habitat -o datascene_datasets/mp3d

• Download R2R dataset:

gdown https://drive.google.com/uc?id=1T9SjqZWyR2PCLSXYkFckfDeIs6Un0Rjm

gdown https://drive.google.com/uc?id=1fo8F4NKgZDH-bPSdVU3cONAkt5EW-tyr

Running and Evaluating Agents

Once you have everything set up, it’s time to run an agent and evaluate its performance:

• Run the agent:

python run.py --exp-config path_to_experiment_config.yaml --run-type train

• Evaluate the agent:

python run.py --exp-config path_to_experiment_config.yaml --run-type eval

Troubleshooting Common Issues

If you run into problems during your setup or while running your agent, here are a few common issues and potential solutions:

• Error: Environment Not Found

  Ensure that you have activated your conda environment using conda activate vlnce.

• Error: Dependency Conflicts

  Try reinstalling the dependencies in a fresh conda environment to avoid any conflicts.

• Error: CUDA Not Detected

  Ensure that your machine has GPUs installed, and they are configured correctly. You may also need to check your CUDA installation.

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

Conclusion

Setting up VLN-CE may seem daunting at first, but by following this guide systematically, you’ll be well on your way to exploring the fascinating field of Vision-and-Language Navigation. Have fun experimenting and discovering the capabilities of your AI agent!

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.