ConBaT: Control Barrier Transformer for Safe Policy Learning

Understand

Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains.

• However, using such models for autonomous systems is challenging because of safety requirements: besides executing correct actions, an autonomous agent must also avoid the high cost and potentially fatal critical mistakes.
• Traditionally, self-supervised training mainly focuses on imitating previously observed behaviors, and the training demonstrations carry no notion of which behaviors should be explicitly avoided.
• In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion.