  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. ConBaT is inspired by the concept
of control barrier functions in control theory and uses a causal transformer
that learns to predict safe robot actions autoregressively using a critic that
requires minimal safety data labeling. During deployment, we employ a
lightweight online optimization to find actions that ensure future states lie
within the learned safe set. We apply our approach to different simulated
control tasks and show that our method results in safer control policies
compared to other classical and learning-based methods such as imitation
learning, reinforcement learning, and model predictive control.
