VLN-CE

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VLN-CE is an AI Agents & Automation tool that facilitates Vision-and-Language Navigation in Continuous Environments using Habitat. It provides baseline agents and training methods for instruction-guided navigation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is VLN-CE?

VLN-CE (Vision-and-Language Navigation in Continuous Environments) is an open-source project designed for instruction-guided navigation tasks within realistic, continuous environments using the Habitat platform. It supports datasets like Room-to-Room (R2R) and Room-Across-Room (RxR), offering both baseline agents and training methodologies. Researchers can leverage VLN-CE to develop and evaluate AI agents capable of navigating complex 3D environments based on natural language instructions. The project provides comprehensive setup instructions, data download links for scenes and episodes, and pre-trained encoder weights, making it a robust starting point for advanced research in embodied AI and robotics.

Best used for

Ideal for researchers and developers who need to implement and test instruction-guided navigation agents in realistic 3D environments, develop embodied AI solutions, and participate in challenges like RxR-Habitat. Especially valuable for those working with vision-and-language navigation tasks and requiring robust simulation platforms.

Common actions

develop navigation agents

evaluate AI models

simulate embodied AI

train language-guided robots

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Capabilities

Key features

Instruction-guided navigation
Continuous environments
Habitat platform integration
R2R dataset support
RxR dataset support
Baseline agents
Training methods

Target Audience

professorresearcherdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What environments does VLN-CE support for navigation tasks?

VLN-CE primarily uses Matterport3D (MP3D) scene reconstructions, providing realistic and continuous environments for instruction-guided navigation. These environments are integrated through the Habitat platform, allowing for complex and unconstrained agent movement within detailed 3D spaces.

Which datasets are compatible with VLN-CE for training and evaluation?

VLN-CE supports both the Room-to-Room (R2R) and Room-Across-Room (RxR) datasets. These datasets provide crowdsourced instructions and varied paths, enabling comprehensive training and evaluation of navigation agents in continuous environments.

What are the hardware requirements for running VLN-CE?

VLN-CE is developed with Python 3.6 and requires Habitat-Sim 0.1.7. For optimal performance, it is recommended to use one GPU for the model and several GPUs for simulation, with approximately one CPU core per environment for faster training and evaluation.

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