Tyler Han

Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation

This work constraining reward function candidacy during IRL to the space of CBFs yields a formulation that exhibits safe online control with continuous experiential improvement, and demonstrates that the recovered barrier function is robust to unsafe states entirely absent from the expert data.