AI Habitat
AI Habitat is an open-source simulation platform from Meta AI Research for embodied AI research. It provides high-performance 3D simulated environments for training and evaluating AI agents on navigation, manipulation, and human-robot collaboration tasks. Habitat-Sim delivers 10,000+ FPS simulation and Habitat-Lab provides a modular library for defining tasks, training agents, and running benchmarks.
APIs
AI Habitat
AI Habitat simulation framework for embodied AI research, including Habitat-Sim (high-performance 3D simulator) and Habitat-Lab (modular training library). Supports navigation, ...
Features
Habitat-Sim achieves 10,000+ FPS on a single GPU and 8,000+ steps/second for robot simulation, enabling fast RL training.
Supports HM3D, MatterPort3D, Gibson, Replica, and HSSD datasets with high visual fidelity.
Bullet physics engine integration for realistic object interactions and manipulation tasks.
Configurable robot models including Fetch mobile manipulator, Franka arm, and AlienGo quadruped.
RGB, depth, semantic, and egomotion sensors for varied agent perception configurations.
Habitat-Lab provides modular task definition, agent configuration, and benchmarking tools.
Built-in support for IL and RL training pipelines for embodied AI agents.
Habitat 3.0 co-habitat supports humans, avatars, and robots sharing simulated environments.
Designed for large-scale distributed training across GPU clusters.
Habitat Challenge on EvalAI provides standardized evaluation of navigation and manipulation agents.
Use Cases
Train and evaluate AI agents on point-goal, object-goal, and image-goal navigation tasks in 3D environments.
Develop manipulation skills for pick-and-place, rearrangement, and tool use with simulated robot arms.
Research human-robot teaming for household tasks using the PARTNR benchmark and Habitat 3.0.
Fast simulation enables RL agents to explore millions of environment steps for policy learning.
Generate synthetic data, annotations, and demonstrations for embodied AI training datasets.
Integrations
Deep learning framework integration for neural network training and inference.
Datasets and models available on HuggingFace Hub at ai-habitat organization.
Habitat Challenge evaluation hosted on EvalAI platform for standardized benchmarking.
Conda package distribution via conda-forge and aihabitat channels.
Bullet physics engine for realistic rigid-body simulation and manipulation.
Robot Operating System integration for sim-to-real transfer research.