Safety-Guided Imitation Learning for Robust and Reliable Robot Behaviors

The core message of this work is to strategically expose the expert demonstrator to safety-critical scenarios during data collection, in order to enhance the safety and robustness of the learned imitation policy, especially in low-data regimes where the likelihood of error is higher.