# Эксперимент: Neural Surrogate for Worst-Case Barrier Drift (#984)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built and ran a 2D double-integrator neural robust-drift surrogate with teacher labels, held-out beta calibration, and safety filtering. The exact correction scaled linearly with error radius at predicted slope -3.04 (observed -3.040000), coefficient sweeps matched predicted slopes exactly, and the MLP learned slope -3.03397. Removing beta produced 0.925% violations versus 0.350% with beta, while exact filtering had 0% violations; surrogate inference was 16.8x faster than 256-sample inner minimization.",
  "metrics": {
    "baseline": "Exact robust correction: 0% held-out violations, mean action 0.75324, 9.376 ms per 512 evaluations for 256 sampled perturbations.",
    "idea": "Neural surrogate with beta: 0.350% held-out violations, mean action 0.75473, 0.559 ms per 512 evaluations; 16.8x speedup. Without beta: 0.925% violations. Exact correction slope prediction -3.04 versus observed -3.040000; learned slope -3.03397."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only a 2D toy double-integrator was tested; no 12D quadrotor, physical rollout, 10,000-trajectory evaluation, differentiable trajectory fine-tuning, collision geometry, or formal safety guarantee was implemented. The affine toy barrier makes the exact correction analytically available; the latency comparison uses sampled perturbation minimization as the expensive reference."
}