Two-track plan: a deterministic VQ1 completion stack (YOLO + PnP + PID) ships first to lock in a passing submission. APEX perception-aware PPO takes over for VQ2 speed. No GPS. No absolute positioning. FPV + telemetry in, Throttle / Roll / Pitch / Yaw out. Per the confirmed 2026-04-19 AIGP spec.
The reusable drone Palmer Luckey describes in his West Point interview, engineered end-to-end: vision-only navigation (no GPS, no datalink), survivable against the laser + HPM + kinetic counter-UAS triad, mass-manufacturable in a car or ag-implement factory — and recoverable: the airframe, seeker, and compute come home to refuel, rearm, and re-fly. Only the munition is spent. Brain = the AIGP vision-autonomy stack. Every requirement traces to a verbatim interview quote.
Same input surface and same output surface for both tracks. Only the controller changes.
The opinionated master strategy. Effort budget across stages, reliability math, data pipeline moat, sim-to-real bridge, compute envelope, explicit anti-patterns, scorecard.
Tactical expansion of the playbook. VQ1 vs VQ2 stacks, training pipeline, detector choice, retired components, Sim Day 1 checklist, intel.
Three-phase training — YOLO11n detector → YOLO11n-pose keypoints → perception-aware PPO. Phase 3 observation-swap flag required for VQ2 transfer.
What changed in the 2026-05-08 spec revision (camera intrinsics, UDP vision stream, ODOMETRY removed) and how the code was aligned on 2026-05-12.
VADR-TS-002 compliance checklist, 3-tier training strategy, submission pipeline, risk matrix.
End-to-end pipeline diagram, component file map, sensor budget, commands surface.
Six states, transition rules, latency budget for a single frame-to-command cycle.
How the APEX detector chain + telemetry adapter + controller bolt into the sim client.
Detector choices (YOLO11n, RF-DETR), dataset layout, evaluation harness, confidence thresholds.
Local Windows / FastAPI dashboard. Live GPU telemetry, one-click controls, run history, live log tail. Double-click launch_trainer_app.bat.
Exact training commands + the overnight_autotrainer.py CLI. Unattended nightly runs with precheck, backup, benchmark, auto-promotion, rollback.
Two-track plan: VQ1 completion stack + VQ2 APEX PPO with observation swap. Parallel sim instances, dataset expansion for distractors.
Auto-label training data by screen-capturing DCL The Game while you play. Closest VQ2-realistic imagery we can get pre-sim. ~3-5K frames per 2-4 hr session.
Feed the detector gate-lookalike images (arches, fans, scaffolding) with empty labels to suppress VQ2 false positives. Keyword-based harvest + auto false-positive discovery.
Three-tier plan to close the "nothing handles obstacles" gap the AIGP spec exposes. Headless Playwright + race-r3f.html produces auto-labeled nc=2 training data. Free synthetic imagery.
One-command APEX run on the RTX 5080 box (~7.5 hr overnight). Outputs, weights, troubleshooting.
Detector benchmark harness — YOLO11n vs RF-DETR vs U-Net on the same course proxy.
Gains, thresholds, timeouts. One source of truth for PID + PPO hyperparameters.
Windows-only sim, anti-cheat internet, T/R/P/Y outputs, nine-check submission list, portal TBD.
Clone, install deps, smoke test, first training run. For contributors joining the repo fresh.
Minimum viable run: detector weights in, frame in, command out. Single-file reproduction.
CUDA OOM, observation-mode confusion, submission-validator failures, anti-cheat handshakes.
Full BOM, frame, FCU, ESCs, motors, cameras. Practice-rig recipe for physical-round prep.
Printable carrier shell for the Neros Archer platform (supplied at physical qualifier).
Mounts, camera brackets, antenna holders — everything printable for the build.
Transmitter + receiver combos validated for development. Protocol + latency notes.
Assembly steps, soldering order, first-flight checklist. Updated for the 2026 practice rig.
Dimensioned drawings for frame + camera mount. Source of truth for BOM disputes.
How we win. Effort budget, reliability math, data pipeline moat, sim-to-real, anti-patterns, scorecard.
Deployed dashboard URLs, Cloudflare Pages, Workers, upstream AIGP resources.
Phase-by-phase details: detector, keypoints, perception-aware PPO. Observation schemas for both modes.