# Эксперимент: Adaptive CBF Safety Layer for Neural Policies (#356)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a scalar adaptive CBF safety filter with bounded action saturation, exponentially smoothed model mismatch, adaptive barrier shrinkage, and a closed-form QP projection. The robust-residual check passed, exponential smoothing predictions matched observations exactly, and predicted adaptive-margin crossing times matched for kappa > 1. Increasing kappa monotonically increased intervention and reduced violations from 87.9 at kappa=0 to 0 at kappa>=1, outperforming static clipping, so the mechanism manifested in this toy setting.",
  "metrics": {
    "baseline": "Static action clipping: mean violations 92.0/100 steps; mean maximum state 11.98",
    "idea": "Adaptive CBF at kappa=2: mean violations 0.0/100 steps, mean maximum state 0.592, mean intervention 114.01; across kappa 0 to 4, violations decreased 87.9 to 0 while intervention increased 106.31 to 121.71"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 adaptive_cbf_experiment.py",
  "files": [
    "adaptive_cbf_experiment.py",
    "results.json"
  ],
  "limitations": "This is a scalar deterministic toy experiment, not PPO, CartPole, a learned neural dynamics model, or a full numerical QP. It does not test stochastic disturbances, multidimensional barrier intersections, model retraining, return/performance tradeoffs, or calibrated error quantiles; the persistent boundary-seeking nominal policy also makes intervention costs unusually large."
}