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name: policies-and-models description: "Choose and inspect policy and model families, interface contracts, checkpoints, and normalization behavior for diffusion-policy." disable-model-invocation: true metadata: disco-role: operating license: MIT

Policies and Models

Use this sub-skill when you need to choose a policy family, inspect its inference API, load or compare checkpoints, or debug model-level import and shape issues.

Covers

• Base low-dim and image policy contracts
• Diffusion UNet and Transformer backbones
• Low-dim, image, hybrid, Robomimic, BET, and IBC policy families
• Normalizers and checkpoint-loaded model state
• Model-specific troubleshooting for optional dependencies and device mismatches

Route elsewhere

• Dataset construction, ReplayBuffer, and sampling windows -> ../data-and-replay-buffers/
• Training, evaluation, multirun, and benchmark orchestration -> ../training-and-evaluation/
• Real camera or robot acquisition and actuation -> ../real-robot-operations/

Start here

1. Read references/policies-and-models.md for family selection and config examples.
2. Read references/api-reference.md for signatures, shapes, and notable methods.
3. Read references/troubleshooting.md for common failure modes and checks.
4. Run scripts/inspect_policy_interfaces.py to print representative class signatures when optional dependencies are installed.

Usage hints

• Always match the policy family to the observation structure: vector-only, image-only, mixed image+vector, Robomimic wrapper, discrete latent baseline, or energy-based baseline.
• Treat shape_meta, horizon, n_obs_steps, and n_action_steps as the first thing to verify before touching model code.
• If a constructor import fails, treat it as a dependency or optional-backend issue rather than a model bug until the troubleshooting checklist says otherwise.