论文标题
可到达的多面体游行（RPM）：具有深神经网络组件的机器人系统的安全验证算法
Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components
论文作者
论文摘要
我们提出了一种用于使用整流线性单元（RELU）激活的深神经网络计算精确到达集的方法。我们的方法非常适合用于具有深层神经网络组件的机器人感知和控制系统的严格安全分析。我们的算法可以计算在多个时间步长迭代的Relu网络的前向和向后触及的集合，正如机器人系统中的感知循环中所示。我们的算法是独一无二的，因为它通过在输入空间中逐步枚举多面体单元格，而不是像其他方法中通过网络逐层迭代。如果发现不安全的单元格，我们的算法可以返回此结果而无需完成完整的可及性计算，从而提供了加速安全验证的任何时间属性。此外，与现有内存可能是限制因素的现有方法相比，我们的方法需要更少的内存。我们演示了有关ACAS XU飞机咨询系统安全验证的算法。我们发现不安全的动作比现有方法最快的速度要快很多次，并且证明在现有方法时间的两倍之内没有不安全的动作。我们还为在笔记本电脑上87S的50个时间步长上学习的超摆动力学模型计算了向前和向后的可触及套件。算法源代码：https：//github.com/stanfordmsl/neural-network-rach。
We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterated over multiple time steps, as would be found in a perception-action loop in a robotic system. Our algorithm is unique in that it builds the reachable sets by incrementally enumerating polyhedral cells in the input space, rather than iterating layer-by-layer through the network as in other methods. If an unsafe cell is found, our algorithm can return this result without completing the full reachability computation, thus giving an anytime property that accelerates safety verification. In addition, our method requires less memory during execution compared to existing methods where memory can be a limiting factor. We demonstrate our algorithm on safety verification of the ACAS Xu aircraft advisory system. We find unsafe actions many times faster than the fastest existing method and certify no unsafe actions exist in about twice the time of the existing method. We also compute forward and backward reachable sets for a learned model of pendulum dynamics over a 50 time step horizon in 87s on a laptop computer. Algorithm source code: https://github.com/StanfordMSL/Neural-Network-Reach.
