---

name: habitat-lab description: "Use Habitat-Lab, Habitat-Baselines, and Habitat-HITL for embodied AI configs, environments, datasets, training, HITL apps, and extensions." disable-model-invocation: true metadata: disco-role: operating license: MIT

Habitat-Lab Repo Skill

Use this repo skill when the user asks about Habitat-Lab, Habitat-Baselines, Habitat-HITL, embodied AI tasks, Habitat configs, navigation or rearrangement environments, baseline training/evaluation, HITL viewers/apps, or Habitat extension registries.

Start Here

• Read references/installation.md before recommending installs, imports, or optional backend packages.
• Read references/data-and-assets.md when a task mentions scenes, episode datasets, data/, dataset downloads, or missing assets.
• Read references/troubleshooting.md for cross-cutting import, Habitat-Sim, graphics, Gym, Hydra, data, and backend failures.
• Read references/repo-provenance.md before deciding whether this skill is stale for a current checkout.
• Use scripts/check_habitat_install.py and scripts/habitat_env_report.py for safe import/config/backend diagnostics.

Route By Task

User task	Read
Install Habitat-Lab, choose Python/Habitat-Sim packages, compose Hydra configs, validate data paths	setup-and-configuration
Create habitat.Env, RLEnv, Gym wrappers, VectorEnv/ThreadedVectorEnv, task configs, datasets, episode flows	tasks-datasets-and-envs
Run or inspect Habitat-Baselines train/eval, Hydra CLI, checkpoints, metrics, PPO/DDPPO/VER/IL configs, benchmarks	baselines-training-and-evaluation
Work with Habitat-HITL realtime viewers, app states, GUI input, websockets, VR/web clients, policy-in-app loops	hitl-apps-and-interaction
Add or debug custom sensors, measures, actions, tasks, datasets, simulators, policies, trainers, obs transforms	extension-patterns

Public Package Facts

• Habitat-Lab is a Python embodied AI framework built on Habitat-Sim for task definition, environment construction, datasets, agents, and benchmarks.
• The source tree exposes three installable distributions: habitat-lab, habitat-baselines, and habitat-hitl; this skill was generated against version 0.3.3 for all three.
• Core imports include habitat, habitat_baselines, and habitat_hitl; simulator-backed imports also require habitat_sim and magnum from Habitat-Sim.
• The repository README documents Python 3.9 with conda and habitat-sim withbullet as the safest public install route for current Habitat-Sim compatibility.
• The habitat-baselines console script maps to habitat_baselines.run:main and is Hydra-powered.

Minimal Public Install Shape

Use this as the starting point when the user has not provided an environment:

conda create -n habitat python=3.9 cmake=3.14.0
conda activate habitat
conda install habitat-sim withbullet -c conda-forge -c aihabitat
pip install -e habitat-lab
pip install -e habitat-baselines  # only when baseline training/eval is needed
pip install -e habitat-hitl       # only when HITL apps are needed

For package inspection or config-only help, do not run training, benchmarks, viewers, or dataset downloads unless the user asks and the data/graphics/hardware requirements are clear.

Safe Diagnostics

python path/to/habitat-lab/scripts/check_habitat_install.py --include-baselines --include-hitl
python path/to/habitat-lab/scripts/habitat_env_report.py --json

Then use narrower sub-skill probes:

python path/to/habitat-lab/sub-skills/setup-and-configuration/scripts/config_probe.py --kind habitat --config benchmark/nav/pointnav/pointnav_habitat_test.yaml
python path/to/habitat-lab/sub-skills/baselines-training-and-evaluation/scripts/baselines_cli_probe.py groups
python path/to/habitat-lab/sub-skills/extension-patterns/scripts/inspect_registry_extensions.py --all

Safety Defaults

• Treat full simulation, Gym environments, HITL apps, benchmarks, and baseline training as data/backend-dependent operations.
• Prefer config composition, import checks, CLI --help, and registry introspection before launching expensive or graphics-heavy workflows.
• Do not rely on the original repository docs, examples, tests, or scripts at runtime; this skill distills the necessary routes, references, and safe probes into its own files.
• If a current checkout has changed from references/repo-provenance.md, run refresh-repo-skill before trusting exact API/config coverage.