Habitat Sim
A flexible, high-performance 3D simulator for Embodied AI research.
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What is Habitat Sim?
AI Habitat (Habitat Sim) is a simulation platform for research in Embodied AI, designed to train virtual robots and egocentric assistants in highly photorealistic and efficient 3D environments before transferring learned skills to reality. The platform consists of three main components: Habitat-Sim (a high-performance physics-enabled 3D simulator), Habitat-Lab (a modular high-level library for end-to-end embodied AI development), and Habitat Challenge (an annual autonomous navigation competition).
Habitat-Sim is a flexible, high-performance 3D simulator supporting 3D scans of indoor/outdoor spaces (HM3D, MatterPort3D, Gibson, Replica), CAD models (ReplicaCAD, YCB, Google Scanned Objects), configurable sensors (RGB-D cameras, egomotion sensing), robots described via URDF (Fetch, Franka, AlienGo), and rigid-body mechanics via Bullet. It achieves several thousand FPS single-threaded and over 10,000 FPS multi-process on a single GPU, prioritizing simulation speed over breadth of capabilities.
Habitat-Lab enables defining embodied AI tasks (navigation, rearrangement, instruction following, question answering, human following), configuring diverse embodied agents (commercial robots, humanoids), training via imitation or reinforcement learning, and benchmarking with standard metrics. It includes human-in-the-loop interaction frameworks and supports single and multi-agent training.
This tool is for embodied AI researchers, robotics researchers, computer vision scientists, and AI engineers working on simulation-to-reality transfer, deep reinforcement learning, robotic navigation, and home assistant development. It empowers a paradigm shift from internet AI based on static datasets to embodied AI where agents act within realistic environments.
Habitat Sim pricing
Pricing model: Freemium
Completely free and open source under MIT license. Habitat-Sim and Habitat-Lab are both MIT licensed with no paid tiers. All features including GPU-accelerated rendering, physics simulation, dataset support, and training algorithms are available for free. Conda packages for stable releases and nightly builds are free. Docker containers for Habitat Challenge are free. Some datasets have separate licensing terms (MatterPort3D and Gibson use CC BY-NC-SA 3.0 US). The Habitat Challenge is free to participate in with code submissions evaluated on EvalAI platform.
Habitat Sim pros
- Extremely fast simulation: over 10,000 FPS multi-process on single GPU
- Physics-enabled with Bullet physics for rigid-body dynamics
- Supports 8,000+ steps per second for Fetch robot in ReplicaCAD
- Built-in support for major datasets: HM3D, MatterPort3D, Gibson, Replica
- Supports CAD models including ReplicaCAD, YCB, Google Scanned Objects
- Configurable sensors: RGB-D cameras, egomotion sensing, semantic segmentation
- Supports multiple robot types: Fetch, Franka arms, AlienGo quadrupeds via URDF
- GPU-accelerated rendering for photorealistic indoor environments
- Open source under MIT license, completely free to use
- Habitat-Lab provides modular high-level API for end-to-end development
- Supports single and multi-agent training scenarios
- Includes reinforcement learning baselines (PPO) out of the box
- Gym-compatible interface for easy integration with ML frameworks
- Annual Habitat Challenge with code submission and novel environment evaluation
- Strong research community with 3.2k stars on habitat-sim GitHub
- Comprehensive documentation with tutorials, Jupyter notebooks, and Colab
- Supports sim2real transfer for deploying learned skills to physical robots
- Conda packages available for stable releases and nightly builds
- Docker containers provided for Habitat Challenge reproducibility
- Human-in-the-loop framework for data collection and agent interaction
Habitat Sim cons
- Design prioritizes speed over breadth of simulation capabilities
- Limited physics fidelity compared to specialized physics simulators
- Complex installation process requiring conda and specific dependencies
- Headless installation required for cluster/multi-GPU setups (EGL)
- MacOS incompatible with EGL-based headless mode
- Interactive testing fails on Ubuntu 20.04 with X11 errors
- VectorEnv errors can be silenced and hard to debug without HABITAT_ENV_DEBUG
- Requires Python >= 3.9 and cmake >= 3.14, limiting older system compatibility
- Datasets like MatterPort3D have restrictive CC BY-NC-SA licensing
- Physical interaction simulation may lack realism for delicate manipulation tasks
Frequently asked questions about Habitat Sim
What is AI Habitat and what does it do?
AI Habitat is a simulation platform for research in Embodied AI. It enables training of embodied AI agents (virtual robots and egocentric assistants) in a highly photorealistic and efficient 3D simulator before transferring learned skills to reality. The platform empowers a paradigm shift from internet AI based on static datasets to embodied AI where agents act within realistic environments, bringing forward active perception, long-term planning, learning from interaction, and environment-grounded dialogue.
Why use simulation instead of training in the real world?
Training embodied AI agents in the real world is slow (runs no faster than real-time and cannot be parallelized), dangerous (poorly-trained agents can inj themselves, humans, or the environment), expensive (both agent and environments are costly), and difficult to control/reproduce (replicating conditions or corner-cases is hard). Simulations run orders of magnitude faster than real-time, can be parallelized over clusters, are safe, cheap, and enable fair systematic benchmarking. Promising approaches developed in simulation can be transferred to physical platforms.
What are the main components of the Habitat platform?
Habitat consists of three main components: (1) Habitat-Sim - a high-performance physics-enabled 3D simulator with support for 3D scans, CAD models, configurable sensors, URDF robots, and Bullet physics; (2) Habitat-Lab - a modular high-level library for end-to-end embodied AI development including task definition, agent configuration, training, and benchmarking; (3) Habitat Challenge - an annual autonomous navigation challenge where participants upload code (not predictions) and agents are evaluated in novel unseen environments to test generalization.
How fast is Habitat-Sim compared to other simulators?
Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded when rendering MatterPort3D scenes and reaches over 10,000 FPS multi-process on a single GPU. For physics simulation, Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each step involves rendering one RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec. The design philosophy prioritizes simulation speed over breadth of simulation capabilities.
What datasets and environments are supported?
Habitat-Sim has built-in support for HM3D, MatterPort3D, Gibson, Replica, and other datasets for 3D scans of indoor/outdoor spaces. It also supports CAD models of spaces and piecewise-rigid objects including ReplicaCAD, YCB, and Google Scanned Objects. Testing scenes (habitat-test-scenes) are available for basic testing, while the fully interactive ReplicaCAD apartment dataset (140 MB) with 105 scene variations with pre-baked lighting (480 MB) is available for physics-enabled simulation.
What robots and agents can be simulated?
Habitat-Sim supports robots described via URDF including mobile manipulators like Fetch, fixed-base arms like Franka, and quadrupeds like AlienGo. Habitat-Lab enables configuring diverse embodied agents including commercial robots and humanoids, with customizable sensors and capabilities. The platform supports both single-agent and multi-agent scenarios.
What sensors are available in Habitat-Sim?
Habitat-Sim provides configurable sensors including RGB-D cameras, egomotion sensing, and semantic segmentation sensors. The sensor suite is flexible and can be customized for different research needs. Noise models are available including the Redwood Depth Noise Model and PyRobot noise models for realistic sensor simulation.
How do I install Habitat-Sim and Habitat-Lab?
Habitat-Sim is best installed via Conda (recommended): create a conda env with Python >= 3.9 and cmake >= 3.14, then install with 'conda install habitat-sim withbullet -c conda-forge -c aihabitat' for physics support. For headless machines, use 'conda install habitat-sim headless'. Habitat-Lab is installed by cloning the stable branch and running 'pip install -e habitat-lab' followed by 'pip install -e habitat-baselines' for RL baselines. Alternatives include PIP (experimental), Docker, and source installation.
What tasks can I build with Habitat-Lab?
Habitat-Lab supports flexible task definitions including navigation, rearrangement, instruction following, question answering, human following, and custom novel tasks. It supports both single-agent and multi-agent tasks. The library provides algorithms for training via imitation learning, reinforcement learning, or classical SensePlanAct pipelines without learning. Standard metrics are available for benchmarking performance on defined tasks.
What is the Habitat Challenge and how does it work?
The Habitat Challenge is an annual autonomous navigation challenge hosted on the EvalAI platform that benchmarks and accelerates progress in embodied AI. Unlike classical image dataset-based challenges (ImageNet, COCO, VQA), participants upload code not predictions, and agents are evaluated in novel unseen environments to test generalization. The challenge started in 2019 at CVPR and has grown significantly: 2021 had 45 teams with 400 submissions, 2020 had 27 teams with 563 submissions, and 2019 had 16 teams with 150 submissions. It is held in conjunction with the Embodied AI workshop at CVPR.