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.

翻译：安全学习对于AI赋能的机器人至关重要，单个故障就可能导致灾难性后果。基于屏障的方法是安全机器人学习的主流方法之一。然而，该方法扩展性不足、难以训练，且在噪声输入下容易产生不稳定信号，难以部署于机器人。为解决这些挑战，我们提出了一种新颖的注意力屏障网络（ABNet），能够以增量方式构建更大的基础安全模型。ABNet中每个BarrierNet头部可从不同特征学习安全的机器人控制策略，并专注于观测的特定部分。通过这种方式，我们无需为复杂任务一次性构建大型模型，这显著促进了模型训练，同时确保了其输出稳定性。最重要的是，我们仍能形式化地证明ABNet的安全性保证。我们在2D机器人避障、安全机器人操作以及基于视觉的端到端自动驾驶任务中展示了ABNet的优势，结果表明其相比现有模型具有更好的鲁棒性与安全性保证。