  Safe learning is central to AI-enabled robots where a single failure may lead
to catastrophic results. Barrier-based method is one of the dominant approaches
for safe robot learning.
  However, this method is not scalable, hard to train, and tends to generate
unstable signals under noisy inputs that are challenging to be deployed for
robots. To address these challenges, we propose a novel Attention BarrierNet
(ABNet) that is scalable to build larger foundational safe models in an
incremental manner.
  Each head of BarrierNet in the ABNet could learn safe robot control policies
from different features and focus on specific part of the observation. In this
way, we do not need to one-shotly construct a large model for complex tasks,
which significantly facilitates the training of the model while ensuring its
stable output. Most importantly, we can still formally prove the safety
guarantees of the ABNet. We demonstrate the strength of ABNet in 2D robot
obstacle avoidance, safe robot manipulation, and vision-based end-to-end
autonomous driving, with results showing much better robustness and guarantees
over existing models.
