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name: setup-and-configuration description: "Install Habitat-Lab, compose Habitat/Habitat-Baselines Hydra configs, validate data paths, and diagnose setup failures." disable-model-invocation: true metadata: disco-role: operating license: MIT

Setup And Configuration

Use this sub-skill when a user needs Habitat-Lab installed or imported, needs to choose a compatible Python/Habitat-Sim path, needs to compose or override Habitat or Habitat-Baselines configs, or needs to diagnose config/data/import failures before running environments or training.

Fast Routing

• For installation, Python/Habitat-Sim compatibility, package order, and data directory layout, read references/install-and-data.md.
• For habitat.get_config, habitat_baselines.config.default.get_config, Hydra config groups, override syntax, read_write, and config validation patterns, read references/configuration.md.
• For concrete failure symptoms such as missing magnum, missing habitat_sim, Hydra errors, invalid override keys, missing datasets/assets, Gym warnings, and graphics/EGL warnings, read references/troubleshooting.md.
• To test imports and config composition without starting simulation or training, run scripts/config_probe.py.

Workflow

1. Confirm the environment goal: lightweight config inspection, environment stepping, baselines training, or HITL use. Prefer a Python 3.9 conda environment when Habitat-Sim wheels/conda packages are needed.
2. Install Habitat-Sim with compatible physics support before relying on habitat imports that transitively need habitat_sim/magnum; then install habitat-lab, and install habitat-baselines only when training/evaluation configs are needed.
3. Use scripts/config_probe.py to verify package imports and compose a known config with any requested overrides before launching expensive simulation or training.
4. Resolve data and asset paths from composed config keys such as habitat.dataset.data_path, habitat.dataset.scenes_dir, habitat.simulator.scene, and robot/physics asset paths.
5. Route runtime behavior questions to sibling sub-skills: environment stepping and task/dataset use to tasks-datasets-and-envs, baselines training/evaluation to baselines-training-and-evaluation, HITL launch or interaction to hitl-apps-and-interaction, and custom registry/config authoring to extension-patterns.

Safe Probe Examples

python path/to/skills/habitat-lab/sub-skills/setup-and-configuration/scripts/config_probe.py \
  --kind habitat \
  --config benchmark/nav/pointnav/pointnav_habitat_test.yaml \
  --override habitat.environment.max_episode_steps=5

python path/to/skills/habitat-lab/sub-skills/setup-and-configuration/scripts/config_probe.py \
  --kind baselines \
  --config pointnav/ppo_pointnav_example.yaml \
  --override habitat_baselines.total_num_steps=10 \
  --key habitat_baselines.trainer_name \
  --key habitat.environment.max_episode_steps

Boundaries

• This sub-skill covers installation/imports, config composition, config mutation, data/asset path validation, and setup/config troubleshooting.
• It does not explain stepping Env, Gym wrappers, episode semantics, task behavior, or dataset generation; use tasks-datasets-and-envs.
• It does not run training/evaluation loops, tune PPO/DDPPO, or interpret checkpoints; use baselines-training-and-evaluation.
• It does not launch HITL applications; use hitl-apps-and-interaction.
• It does not define new registries, sensors, measures, actions, or structured config classes; use extension-patterns.