AI Habitat website screenshot

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.

AI Habitat publishes 1 API on the APIs.io network. Tagged areas include Artificial Intelligence, Simulation, Embodied AI, Robotics, and Computer-Vision.

The AI Habitat catalog on APIs.io includes 1 JSON-LD context and 1 Spectral governance ruleset.

AI Habitat’s developer surface includes documentation, developer portal, tooling, changelog, CLI, sandbox, API reference, and 28 more developer resources.

34.8/100 thin ▬ flat Agent 3/100 human only Full breakdown ↓
scored 2026-09-10 · rubric v0.20.0
AccessFreemium
1 APIs 10 Features 5 Use Cases
Artificial IntelligenceSimulationEmbodied AIRoboticsComputer-VisionReinforcement LearningMachine-LearningOpen-SourceResearch

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-10 · rubric v0.20.0
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/ai-habitat: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

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, ...

Pricing Plans 1

Published pricing tiers and plan structures.

Rate Limits 1

Documented rate limits and quota policies.

Ai Habitat Rate Limits

0 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Features 10

Notable capabilities this provider offers.

High-Performance Simulation

Habitat-Sim achieves 10,000+ FPS on a single GPU and 8,000+ steps/second for robot simulation, enabling fast RL training.

Photorealistic 3D Environments

Supports HM3D, MatterPort3D, Gibson, Replica, and HSSD datasets with high visual fidelity.

Physics-Enabled Simulation

Bullet physics engine integration for realistic object interactions and manipulation tasks.

Robot Support via URDF

Configurable robot models including Fetch mobile manipulator, Franka arm, and AlienGo quadruped.

Configurable Sensors

RGB, depth, semantic, and egomotion sensors for varied agent perception configurations.

Modular Task Framework

Habitat-Lab provides modular task definition, agent configuration, and benchmarking tools.

Imitation and Reinforcement Learning

Built-in support for IL and RL training pipelines for embodied AI agents.

Human-Robot Collaboration

Habitat 3.0 co-habitat supports humans, avatars, and robots sharing simulated environments.

Parallelizable Across Clusters

Designed for large-scale distributed training across GPU clusters.

Annual Benchmark Challenge

Habitat Challenge on EvalAI provides standardized evaluation of navigation and manipulation agents.

Scroll for all 10

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Ai Habitat Context

7 classes · 13 properties

JSON-LD

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

AI Habitat API Rules

5 rules · 4 warnings 1 info

SPECTRAL

JSON Schema 8

Standalone JSON Schema definitions for this provider's data models.

AgentConfig

5 properties

JSON SCHEMA

AgentObservation

4 properties

JSON SCHEMA

Episode

6 properties

JSON SCHEMA

NavigationGoal

2 properties

JSON SCHEMA

Observation

5 properties

JSON SCHEMA

SensorSpec

6 properties

JSON SCHEMA

SimulatorConfig

7 properties

JSON SCHEMA

TaskConfig

5 properties

JSON SCHEMA

Scroll for all 8

JSON Structure 8

JSON Structure definitions describing this provider's data shapes.

Ai Habitat Agent Config Structure

5 properties

JSON STRUCTURE

Ai Habitat Agent Observation Structure

4 properties

JSON STRUCTURE

Ai Habitat Episode Structure

6 properties

JSON STRUCTURE

Ai Habitat Navigation Goal Structure

2 properties

JSON STRUCTURE

Ai Habitat Observation Structure

5 properties

JSON STRUCTURE

Ai Habitat Sensor Spec Structure

6 properties

JSON STRUCTURE

Ai Habitat Simulator Config Structure

7 properties

JSON STRUCTURE

Ai Habitat Task Config Structure

5 properties

JSON STRUCTURE

Scroll for all 8

Examples 8

Example request and response payloads for these APIs.

Scroll for all 8

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Ai Habitat Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Ai Habitat Vulnerability Disclosure

disclosure policy published

SECURITY

Use Cases 5

What developers build with this provider.

Embodied Navigation Research

Train and evaluate AI agents on point-goal, object-goal, and image-goal navigation tasks in 3D environments.

Robot Manipulation Research

Develop manipulation skills for pick-and-place, rearrangement, and tool use with simulated robot arms.

Human-Robot Collaboration

Research human-robot teaming for household tasks using the PARTNR benchmark and Habitat 3.0.

Reinforcement Learning Training

Fast simulation enables RL agents to explore millions of environment steps for policy learning.

Dataset Creation and Annotation

Generate synthetic data, annotations, and demonstrations for embodied AI training datasets.

Integrations 6

Pre-built integrations with other platforms and tools.

PyTorch

Deep learning framework integration for neural network training and inference.

HuggingFace

Datasets and models available on HuggingFace Hub at ai-habitat organization.

EvalAI

Habitat Challenge evaluation hosted on EvalAI platform for standardized benchmarking.

Conda / conda-forge

Conda package distribution via conda-forge and aihabitat channels.

Bullet Physics

Bullet physics engine for realistic rigid-body simulation and manipulation.

ROS

Robot Operating System integration for sim-to-real transfer research.

Resources

Get Started 4

Portal, sign-up, and the first successful call

Documentation 4

Reference material describing how the API behaves

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 5

Pagination, idempotency, versioning, errors, and events

Build 9

SDKs, sample code, and the tooling you integrate with

Scroll for all 9

Access & Security 3

Authentication, authorization, and security posture

Operate 5

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Company 1

The organization behind the API

Other 2

Properties that don't map to a standard resource type

Source (apis.yml)

apis.yml Raw ↑
aid: ai-habitat
url: https://raw.githubusercontent.com/api-evangelist/ai-habitat/refs/heads/main/apis.yml
apis:
- aid: ai-habitat:ai-habitat
  name: AI Habitat
  tags:
  - Embodied AI
  - Simulation
  - Robotics
  - Python
  - Computer-Vision
  - Reinforcement Learning
  tags_raw:
  - Embodied AI
  - Simulation
  - Robotics
  - Python
  - Computer Vision
  - Reinforcement Learning
  humanURL: https://aihabitat.org/
  properties:
  - url: https://aihabitat.org/
    type: Documentation
  - url: https://aihabitat.org/docs/
    type: Documentation
    title: API Documentation
  - url: https://github.com/facebookresearch/habitat-sim
    type: GitHubRepository
    title: Habitat-Sim GitHub
  - url: https://github.com/facebookresearch/habitat-lab
    type: GitHubRepository
    title: Habitat-Lab GitHub
  - url: https://pypi.org/project/habitat-sim/
    type: SDKs
    title: Habitat-Sim Python Package
  - url: https://pypi.org/project/habitat-lab/
    type: SDKs
    title: Habitat-Lab Python Package
  description: AI Habitat simulation framework for embodied AI research, including Habitat-Sim (high-performance 3D simulator)
    and Habitat-Lab (modular training library). Supports navigation, manipulation, and human-robot collaboration tasks across
    photorealistic 3D indoor environments.
name: AI Habitat
tags:
- Artificial Intelligence
- Simulation
- Embodied AI
- Robotics
- Computer-Vision
- Reinforcement Learning
- Machine-Learning
- Open-Source
- Research
tags_raw:
- Artificial Intelligence
- Simulation
- Embodied AI
- Robotics
- Computer Vision
- Reinforcement Learning
- Machine Learning
- Open Source
- Research
kind: contract
deliveryModel:
  model: unknown
  open_source: unknown
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - repository-unlicensed
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: medium
  source:
  - plans
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/ai-habitat.png
access: 3rd-Party
created: '2025-02-17'
modified: '2026-08-30'
position: Consuming
description: 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.
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
specificationVersion: '0.23'
common:
- type: Website
  url: https://aihabitat.org/
- type: DomainSecurity
  url: security/ai-habitat-domain-security.yml
- name: AI Habitat GitHub Organization
  url: https://github.com/facebookresearch
  type: GitHubOrganization
  description: Meta AI Research GitHub organization hosting Habitat repositories.
- name: Habitat-Sim Repository
  url: https://github.com/facebookresearch/habitat-sim
  type: GitHubRepository
  description: High-performance 3D simulator for embodied AI research (C++/Python).
- name: Habitat-Lab Repository
  url: https://github.com/facebookresearch/habitat-lab
  type: GitHubRepository
  description: Modular library for embodied AI task definition, training, and benchmarking.
- name: Habitat Documentation
  url: https://aihabitat.org/docs/
  type: Documentation
  description: Official documentation for AI Habitat platform.
- name: Habitat Challenge
  url: https://aihabitat.org/challenge/
  type: Portal
  description: Annual Habitat Challenge competition on EvalAI for embodied AI navigation.
- name: Habitat Discussions
  url: https://github.com/facebookresearch/habitat-lab/discussions
  type: Forums
  description: Community forum for Habitat questions and support.
- name: Habitat HuggingFace
  url: https://huggingface.co/ai-habitat
  type: Portal
  description: AI Habitat datasets and models on HuggingFace.
- name: Habitat-Sim PyPI
  url: https://pypi.org/project/habitat-sim/
  type: SDKs
  title: Python Package (habitat-sim)
  description: Install Habitat-Sim via pip or conda.
- name: Habitat-Lab PyPI
  url: https://pypi.org/project/habitat-lab/
  type: SDKs
  title: Python Package (habitat-lab)
  description: Install Habitat-Lab via pip.
- name: PARTNR Planner
  url: https://github.com/facebookresearch/partnr-planner
  type: Tools
  description: PARTNR benchmark for human-robot collaboration using Large Planning Models with Habitat.
- type: Features
  data:
  - name: High-Performance Simulation
    description: Habitat-Sim achieves 10,000+ FPS on a single GPU and 8,000+ steps/second for robot simulation, enabling fast
      RL training.
  - name: Photorealistic 3D Environments
    description: Supports HM3D, MatterPort3D, Gibson, Replica, and HSSD datasets with high visual fidelity.
  - name: Physics-Enabled Simulation
    description: Bullet physics engine integration for realistic object interactions and manipulation tasks.
  - name: Robot Support via URDF
    description: Configurable robot models including Fetch mobile manipulator, Franka arm, and AlienGo quadruped.
  - name: Configurable Sensors
    description: RGB, depth, semantic, and egomotion sensors for varied agent perception configurations.
  - name: Modular Task Framework
    description: Habitat-Lab provides modular task definition, agent configuration, and benchmarking tools.
  - name: Imitation and Reinforcement Learning
    description: Built-in support for IL and RL training pipelines for embodied AI agents.
  - name: Human-Robot Collaboration
    description: Habitat 3.0 co-habitat supports humans, avatars, and robots sharing simulated environments.
  - name: Parallelizable Across Clusters
    description: Designed for large-scale distributed training across GPU clusters.
  - name: Annual Benchmark Challenge
    description: Habitat Challenge on EvalAI provides standardized evaluation of navigation and manipulation agents.
- type: UseCases
  data:
  - name: Embodied Navigation Research
    description: Train and evaluate AI agents on point-goal, object-goal, and image-goal navigation tasks in 3D environments.
  - name: Robot Manipulation Research
    description: Develop manipulation skills for pick-and-place, rearrangement, and tool use with simulated robot arms.
  - name: Human-Robot Collaboration
    description: Research human-robot teaming for household tasks using the PARTNR benchmark and Habitat 3.0.
  - name: Reinforcement Learning Training
    description: Fast simulation enables RL agents to explore millions of environment steps for policy learning.
  - name: Dataset Creation and Annotation
    description: Generate synthetic data, annotations, and demonstrations for embodied AI training datasets.
- type: Integrations
  data:
  - name: PyTorch
    description: Deep learning framework integration for neural network training and inference.
  - name: HuggingFace
    description: Datasets and models available on HuggingFace Hub at ai-habitat organization.
  - name: EvalAI
    description: Habitat Challenge evaluation hosted on EvalAI platform for standardized benchmarking.
  - name: Conda / conda-forge
    description: Conda package distribution via conda-forge and aihabitat channels.
  - name: Bullet Physics
    description: Bullet physics engine for realistic rigid-body simulation and manipulation.
  - name: ROS
    description: Robot Operating System integration for sim-to-real transfer research.
- name: AI Habitat Packages and SDKs
  url: packages/ai-habitat-packages.yml
  type: Packages
  description: PyPI, conda and source distributions for habitat-sim, habitat-lab and habitat-baselines, with versions and
    publish dates.
- name: AI Habitat SDK Inventory
  url: packages/ai-habitat-packages.yml
  type: SDKs
  description: First-party Python/C++ client distributions. The product is the package; there is no hosted API.
- name: AI Habitat llms.txt
  url: llms/ai-habitat-llms.txt
  type: LLMsTxt
  description: Agent-readable orientation to AI Habitat, generated from this profile and its artifacts.
- name: AI Habitat Lifecycle
  url: lifecycle/ai-habitat-lifecycle.yml
  type: Lifecycle
  description: Versioning scheme, release cadence, support channels and end-of-maintenance posture.
- name: AI Habitat End-of-Maintenance Notice
  url: lifecycle/ai-habitat-lifecycle.yml
  type: Deprecation
  description: First-party notice that beyond v0.3.4 the project receives no official active development or maintenance by
    Meta internal teams.
- name: AI Habitat Release History
  url: changelog/ai-habitat-changelog.yml
  type: ChangeLog
  description: Structured release notes for habitat-sim and habitat-lab from v0.3.0 to v0.3.4, read from the GitHub release
    feeds.
- name: AI Habitat Command Line
  url: cli/ai-habitat-cli.yml
  type: CLI
  description: Documented command-line surface — dataset downloader, C++/Python viewers, example and benchmark runners.
- name: AI Habitat Sandbox and Test Fixtures
  url: sandbox/ai-habitat-sandbox.yml
  type: Sandbox
  description: In-browser WebAssembly demo plus the named downloadable test scenes and datasets used as the install smoke
    test.
- name: Habitat Browser Demo
  url: https://aihabitat.org/demo
  type: Playground
  description: WebAssembly build of the simulator that runs in the browser with no install or account.
- name: AI Habitat Conformance
  url: conformance/ai-habitat-conformance.yml
  type: Conformance
  description: Domain-standard conformance — Gym/Gymnasium environment interface, URDF, glTF/GLB, Bullet, Hydra config.
- name: AI Habitat Data Model
  url: data-model/ai-habitat-data-model.yml
  type: DataModel
  description: Entity-relationship graph over the simulator, agent, sensor, observation, episode and task objects.
- name: AI Habitat Vulnerability Disclosure
  url: security/ai-habitat-vulnerability-disclosure.yml
  type: VulnerabilityDisclosure
  description: facebookresearch organization security policy routing reports to the Meta Bug Bounty program.
- name: AI Habitat Security Policy
  url: security/ai-habitat-vulnerability-disclosure.yml
  type: Security
  description: How to report a vulnerability in habitat-sim or habitat-lab — never as a public issue or pull request.
- name: AI Habitat Plans and Pricing
  url: plans/ai-habitat-plans-pricing.yml
  type: Plans
  description: Measured zero — MIT-licensed software with no hosted service, no plans and no pricing page.
- name: AI Habitat Rate Limits
  url: rate-limits/ai-habitat-rate-limits.yml
  type: RateLimits
  description: Measured zero — no network API, therefore no published or observable rate limits.
- name: AI Habitat Vocabulary
  url: vocabulary/ai-habitat-vocabulary.yaml
  type: Vocabulary
  description: Taxonomy for simulation configuration, agent specifications, task definitions and dataset resources.
- name: AI Habitat JSON Schemas
  url: json-schema/ai-habitat-simulator-config-schema.json
  type: JSONSchema
  description: JSON Schema 2020-12 definitions for the Habitat configuration and observation objects.
- name: AI Habitat Spectral Rules
  url: rules/ai-habitat-jsonschema-spectral-rules.yml
  type: Rules
  description: Spectral ruleset measured from this provider's own JSON Schema conventions.
- name: Habitat-Sim API Reference
  url: https://aihabitat.org/docs/habitat-sim/
  type: APIReference
  description: Generated simulator reference including Python classes and the C++ API.
- name: Habitat-Lab API Reference
  url: https://aihabitat.org/docs/habitat-lab/
  type: APIReference
  description: Generated reference for the task, training and benchmarking library.
- name: Habitat Installation Guide
  url: https://github.com/facebookresearch/habitat-sim#installation
  type: GettingStarted
  description: Four documented install paths — conda (recommended), pip from source, Docker, and source build.
- name: Habitat Community Support
  url: https://github.com/facebookresearch/habitat-lab/discussions
  type: Support
  description: The project's stated support channel; GitHub issues cover defects in each repository.
- name: AI Habitat Cookie Policy
  url: https://aihabitat.org/cookie-policy/
  type: CookiePolicy
  description: The site's only published legal/privacy-family document (last updated June 2021); it defers to Meta's Data
    Policy for data processing.
x-enrichment:
  date: '2026-08-30'
  status: enriched
  artifacts_added: 10
  pass: local-v1

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