Tag
This paper introduces a permutation-equivariant neural operator that maps spacecraft, target, and debris distributions to collision-aware trajectories for entire swarms in one forward pass, trained with self-supervised physics objectives and adversarial threats. It generalizes zero-shot from 10 to 1,000 spacecraft amid dense debris, matching optimal-control accuracy while reducing proximity.