DART Simulation Verification

Load this skill when verifying that a DART 3D scene or physics simulation is correct — implementing, debugging, benchmarking, or reviewing dynamics, collision, contact, or GUI output. Modern image-capable agents can inspect a capture, but pixels do not expose solver state and machine image checks are not semantic inspection. This tooling grounds visual reasoning without a GUI.

Lead with text, corroborate with images. Measured A/B evidence: per-step metrics and trajectories detect nearly all seeded physics defects; a rendered image alone misses static geometry defects (penetration, interpenetration). Decide correctness from text; use images for scene comprehension and gross dynamic failures.

Applicability contract

Use this skill for any task whose claim depends on 3D structure or behavior: model/scene loading, dynamics, collision/contact/constraints, simulation stepping, GUI/rendering, or visual examples. First run a text oracle (metrics, scene diff, trajectory/contact comparison, or focused behavioral test), then corroborate it end to end with an assessed headless view and only the debug layers needed by the claim. If rendering is unavailable or genuinely irrelevant, record why and name the replacement evidence; never treat an image as the sole correctness oracle. When the active agent accepts image input, actually open and semantically inspect the selected capture; a passing view report or image-verdict is not visual review.

Full documentation

docs/onboarding/agent-sim-verification.md — the durable guide. docs/ai/verification.md owns the gate policy; docs/onboarding/profiling.md owns text-first profiling.

Image-capable review loop

Current image-capable targets (GPT-5.6 Sol Max in Codex; Claude Fable 5 and Opus 5 in Claude Code) support native image input and original-detail inspection. Keep this loop capability-based so a future model-upgrade audit can replace the target-specific note without cloning the skill.

1. State one claim, its expected visible observation, and the text oracle that decides correctness.
2. Capture one assessed, claim-tied view first. Add only the debug layers needed for the claim; use paired plain/debug views when an overlay could obscure the underlying scene. For a turntable or motion sequence, inspect at least the capture sidecar's start/middle/end frame targets; add intervening frames or a grid when a transient event is part of the claim.
3. Run image-verdict for artifact integrity, then open the selected local PNG with the active agent's native image viewer. Use original detail for small contacts, labels, bounds, or frame axes. Add a grid or another view only when motion, occlusion, or ambiguity requires it.
4. Record the visible observation separately from the text result. If they disagree, do not average them into a pass: report fail/uncertain, inspect the capture sidecar, reframe or recapture, and investigate the simulation state.
5. Close with pass/fail/uncertain, artifact path, view/layers, reproduction command, what the image shows, and what it does not prove. If native image review is unavailable, use verification-bundle for an image-capable reviewer and record that limitation.

Quick commands

Text (primary):

• world.compute_step_metrics() — energy/momentum/penetration/contacts/residual
• dartpy.dump_scene_json(world) / dump_scene_text(world) — "what is in this world?" (glTF/USD-flavored hierarchy + flat index)
• pixi run scene-diff — structural JSON verdict for intended-vs-actual scene dumps
• pixi run trajectory-record / pixi run trajectory-compare — per-body TSV + contact JSONL; bit-exact or tolerance diff with first-divergence

Visual (corroboration):

• dart.gui.render(world, camera=None, size=(w, h), debug=(...layers...)) → headless image with optional world-derived debug layers (grid, world_frame, body_frames, coms, inertia_boxes, collision_bounds, velocities, contacts, labels; trajectories additionally requires a sampled dart.gui.TrajectoryTracker via debug_scene_for_world); dart.gui.render_annotated(...) composites label text; .png_bytes() for notebooks; dart.gui.orbit_camera(...) / look_at(...)
• dart.gui.assess_view(world, camera, size, focus=...) → ViewReport with issues (cropped/too-far/too-close/occluded/ambiguous); dart.gui.select_viewpoints(...) picks deterministic best views; dart.gui.frame_body/frame_region reframe onto a subject. Assess first; fix flagged views before capturing evidence. Descriptor bounds, transformed corners, viewport FOV, and fit distance stay in the shared dart::gui core; Python performs only focus resolution and capture/search orchestration.
• viewer camera flags: --view {three-quarter|front|side|top}, --camera-azimuth/-elevation/-distance/-target, --turntable N, --fit
• pixi run py-demo-capture — headless PNG/MP4 capture from Python
• pixi run agent-capture — deterministic evidence harness: auto/explicit cameras, debug layers, stills/turntable/motion video, reproducible sidecar
• pixi run image-compose — side-by-side / blend / diff-heatmap composites
• pixi run evidence-select — claim-driven artifact selection with recorded rationale; pixi run evidence-publish — PR "Visual verification" section with a required semantic verdict and claim boundary plus GitHub-hosted media (manual placeholders by default; gh-release upload only with --yes + maintainer approval)
• pixi run image-verdict / image-golden / image-sheet — JSON verdict, golden diff, contact sheet (contrast is report-only; --require-contrast to gate); these are machine pixel checks, not semantic visual review
• pixi run image-ab-study — blind-judge detection deltas for single-view, multi-view, turntable, and annotated captures
• pixi run image-ab-round2 — prepare a blinded round-2 packet and score completed judge observations

Opt-in:

• pixi run render-golden-gate — opt-in golden gate (backend-specific golden, curated locally with -- --update; not default CI)
• pixi run rerun-trajectory — rerun.io inspection (opt-in; graceful when rerun-sdk is absent)
• pixi run verification-bundle — package text evidence plus still/grid images for a provider-neutral VLM or reviewer call

Default capture for agent review: one ~1280 px frame, UI hidden, 3/4 view; add a 9-frame grid for motion. Keep images as corroboration, never the sole oracle for static geometry.

DART 6 (release-6.20)

Equivalent capture over OpenSceneGraph: dart::gui::osg::setUpOffscreenViewer / captureOffscreen + dartpy bindings, pixi run capture (needs a real X server or Xvfb), the ported image tooling, and pixi run bm-boxes-headless for rendering-free determinism checksums.