SAGAS learns a reusable latent reachability graph from fixed offline trajectory fragments to synthesize cost-aware accepting plans for unseen linear temporal logic specifications via test-time semantic graph augmentation and Büchi search without online interaction or retraining.
A transformer with shared modular recurrence recovers partial contextual features via modular interactions, substantially improving zero-shot generalization to unseen robot dynamics, kinematics, and topologies.