AI Habitat is a 3D simulation platform for embodied AI—agents that learn to perceive, reason, and act within photo-realistic environments. Developed by Meta AI (formerly Facebook AI Research) and open-sourced for the research community, Habitat enables training and evaluation of AI agents in indoor navigation and interaction tasks using high-fidelity simulation.

Designed for researchers in computer vision, robotics, and reinforcement learning, Habitat supports large-scale experiments in visual navigation, object manipulation, and semantic understanding, using real-world 3D scans and synthetic environments.

Key benefits:

• Fast photorealistic simulation, supporting thousands of frames per second

• Support for navigation, object interaction, and task planning

• Integration with AI frameworks for large-scale learning and benchmarking

What are the main features of AI Habitat?

Realistic 3D environments with high performance

Habitat simulates indoor scenes using datasets like Matterport3D and Replica, providing detailed, lifelike environments.

• Photo-realistic rendering of rooms, textures, and objects

• Real-world scale and spatial layouts for realistic agent behavior

• Thousands of simulation steps per second on modern hardware

Support for embodied navigation and manipulation tasks

Habitat allows training of agents for tasks such as point-goal navigation, room exploration, and object goal search.

• Egocentric vision input with RGB, depth, and semantic segmentation

• Fine-grained action spaces for movement and interaction

• Multi-modal sensor configurations for active perception research

Flexible integration with machine learning pipelines

Habitat provides Python APIs and supports integration with popular AI frameworks.

• Compatible with PyTorch and reinforcement learning libraries

• Includes Habitat Lab for defining tasks, environments, and agent architectures

• Tools for dataset generation, task benchmarking, and evaluation

Modular and extensible design

The platform is built with modularity in mind, allowing researchers to customize environments, agents, and tasks.

• Plugin system for adding sensors, tasks, or new simulation logic

• Easily extendable with custom physics or semantics

• Configurable simulation parameters for controlled experiments

Community-driven and open source

AI Habitat is actively developed and maintained as an open-source project, with contributions from both academia and industry.

• Public benchmarks and leaderboards (e.g., Habitat Challenge)

• Integration with popular datasets and research tools

• Ongoing updates based on cutting-edge research needs

Why choose AI Habitat?

• Purpose-built for embodied AI, with support for visual navigation, planning, and manipulation

• Fast and scalable simulation, enabling massive parallel training

• Photo-realistic indoor environments, based on real-world scans

• Flexible and extensible, for easy integration with research workflows

• Backed by an active research community, including Meta AI and academic partners