从专家演示中利用逆约束学习来学习神经控制屏障函数
Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning
AI总结：
本研究提出利用逆约束学习从专家演示中训练约束函数，以标注模拟轨迹并学习神经控制屏障函数，在四个环境中验证了其优于基线且接近真实标签性能。
摘要：
安全性是运行在关键领域的自主系统的基本要求。控制屏障函数（CBFs）已被用于设计安全过滤器，以最小程度地改变此类系统的名义控制，从而维持其安全性。学习神经CBF已被提出作为数据驱动的替代方案，以取代计算成本高昂的基于优化的综合方法。然而，通常需要避免的失败状态集并不明显或难以正式指定，例如自动驾驶中的尾随行为，而实现任务并避开失败状态的专家演示集则更容易生成。我们使用ICL来训练一个约束函数，该函数将所考虑系统的状态分类为安全的，即属于一个受控前向不变集，该集合与未指定的失败集不相交，以及不安全的，即属于该集合的补集。然后，我们使用该函数来标注一组新的模拟轨迹，以训练我们的神经CBF。我们在四个不同的环境中对我们的方法进行了实证评估，结果表明它优于现有的基线方法，并且与使用相同数据但标注了真实安全标签训练的神经CBF相比，达到了相当的性能。
英文摘要：
Safety is a fundamental requirement for autonomous systems operating in critical domains. Control barrier functions (CBFs) have been used to design safety filters that minimally alter nominal controls for such systems to maintain their safety. Learning neural CBFs has been proposed as a data-driven alternative for their computationally expensive optimization-based synthesis. However, it is often the case that the failure set of states that should be avoided is non-obvious or hard to specify formally, e.g., tailgating in autonomous driving, while a set of expert demonstrations that achieve the task and avoid the failure set is easier to generate. We use ICL to train a constraint function that classifies the states of the system under consideration to safe, i.e., belong to a controlled forward invariant set that is disjoint from the unspecified failure set, and unsafe ones, i.e., belong to the complement of that set. We then use that function to label a new set of simulated trajectories to train our neural CBF. We empirically evaluate our approach in four different environments, demonstrating that it outperforms existing baselines and achieves comparable performance to a neural CBF trained with the same data but annotated with ground-truth safety labels.
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