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name: diffusion-policy description: "Use Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot operations." disable-model-invocation: true metadata: disco-role: operating license: MIT

Diffusion Policy

Use this repo skill when a task involves the diffusion_policy robotics imitation-learning codebase: Hydra experiment configs, Push-T/Robomimic/Kitchen/BlockPush tasks, zarr demonstration replay buffers, diffusion action policies, checkpoint evaluation, Ray multiruns, or real Push-T robot data collection/evaluation.

Start with these checks

1. Read repo provenance before assuming the skill matches a checkout or package version.

2. Read overview and installation for dependency variants, package/import behavior, and safe smoke checks.

3. Run the bundled smoke checker when you need environment evidence without training, downloads, Ray, cameras, or robot motion:

   python scripts/smoke_check.py --json
   

4. Use cross-cutting troubleshooting for import, Hydra, dependency, dataset, CUDA, simulator, W&B, and hardware failures.

Route by task

If the user needs...	Read
Build or debug single-seed training, checkpoint evaluation, Hydra overrides, Ray multiruns, output trees, W&B/logs, or benchmark command structure	training-and-evaluation
Inspect or validate zarr/zip ReplayBuffer stores, dataset sample schemas, SequenceSampler, normalizers, or dataset conversion assumptions	data-and-replay-buffers
Choose/inspect low-dim, image, hybrid, diffusion UNet/Transformer, Robomimic, BET, or IBC policy/model families; debug shape, normalizer, checkpoint, or device issues	policies-and-models
Preflight UR5 + RealSense + SpaceMouse workflows, demo capture, real robot policy evaluation, shared-memory IO, real dataset conversion, or hardware safety gates	real-robot-operations

What this skill covers

• The core Dataset -> Normalizer -> Policy -> EnvRunner -> Workspace workflow.
• Low-dimensional and image observation/action interfaces, including horizon terminology (horizon, n_obs_steps, n_action_steps).
• Hydra workspace/task config composition and command-building patterns.
• ReplayBuffer zarr/zip store structure and episode validation.
• Checkpoint evaluation behavior, including EMA model selection.
• Optional simulator, CUDA, Ray, W&B, Robomimic, MuJoCo, and real-robot dependency boundaries.
• Safety-gated real-robot operations: UR RTDE, RealSense, SpaceMouse, shared-memory queues/ring buffers, timestamp alignment, and conversion of recorded episodes.

Boundaries and safety

• Do not launch training, Ray workers, data downloads, W&B online logging, simulator rollouts, camera capture, or robot motion as a smoke check.
• Do not treat a CPU import check as proof that benchmark-scale CUDA, MuJoCo/Robomimic, or real-robot workflows are ready.
• Stop for explicit operator confirmation before any live robot action, camera recording, or command that may overwrite run outputs.
• When a workflow needs project entrypoints or config files, first verify that the user is operating in a compatible Diffusion Policy checkout or equivalent project layout; this skill provides reusable operating knowledge and safe helpers, not the full benchmark runtime.

Bundled scripts

• scripts/smoke_check.py checks distribution metadata, representative imports, optional config root counts, and optional torch CUDA visibility without side effects.
• Sub-skill helpers validate config composition, summarize multirun logs, inspect ReplayBuffer stores, inspect policy interfaces, and preflight real-robot dependencies.

Refresh and verification notes

This skill was generated from a clean source snapshot before skill-output files were added. If a checkout has a different commit, changed config targets, dependency files, entrypoint options, dataset/policy/workspace classes, or real-robot APIs, refresh the repo skill before relying on detailed commands or signatures.