Conformal CBVF Safety Shield

1. Integration point: train an observation-based policy pi_theta(y), using an RNN, transformer, or recurrent world model, but execute it through a safety layer. The estimator E_phi produces z. Fit or provide a differentiable certificate V(t,x), and use automatic differentiation to obtain its state gradient. 2. Pseudocode: collect N calibration tuples (y_i,x_i), compute r_i=norm(x_i-E_phi(y_i)), and set q to the conformal order statistic. At runtime compute z=E_phi(y), u0=pi_theta(y), generate error candidates e_j by projected optimization inside the ball norm(e)<=q, enumerate disturbance vertices d_k when D is a polytope, and solve a quadratic program minimizing norm(u-u0)_R subject to the barrier derivative inequality for each pair (e_j,d_k). Apply the resulting u and repeat. 3. Computed versus estimated: q is computed directly from held-out residuals; V and f are model-based or learned; the inner worst-case minimization is approximated by projected gradient ascent, random samples, or conservative Lipschitz bounds. 4. First experiment: use a two-dimensional point-mass obstacle-avoidance simulator with noisy position observations, an MLP or GRU policy, and compare an unshielded policy with a nominal barrier filter and the conformal shield. Sweep alpha in {0.01,0.05,0.1,0.2} and calibration size N. The prediction is that empirical estimator coverage is near or above 1-alpha, unsafe-episode frequency decreases as alpha decreases, and intervention magnitude increases with q. A sharp feasibility boundary should appear when q approaches the obstacle-clearance margin: beyond that point V_hat becomes negative and the QP becomes frequently infeasible or strongly corrective.