Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
This work introduces Discriminative Barrier Functions (DBFs) to constrain reward function learning during Inverse Reinforcement Learning from Observation (IRLfO). By restricting the learned cost function to Discrete-time Control Barrier Functions, DBFs enable safe online control. The method recovers barrier functions directly from expert observations, allowing it to handle unseen obstacles robustly and improve safety compared to standard imitation learning baselines.