Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariance sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method's efficacy on a dual-track robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention.

翻译：由于执行器限制（如力矩约束），在复杂环境中确保机器人安全性是一项艰巨任务。本文提出一种安全关键控制框架，利用基于学习的多个备用控制器之间切换，在满足驾驶员意图的同时，形式化保证有界控制输入下的安全性。通过利用旨在维护安全性和输入约束的备用控制器，备用控制障碍函数通过可行二次规划构建隐式定义的控制不变集。然而，备用控制障碍函数的性能在很大程度上取决于所选备用控制器的设计和保守性，特别是在复杂（如非铺装路面）条件下的人类驾驶车辆场景中。虽然采用多个备用控制器可以降低保守性，但确定切换时机仍是一个开放性问题。因此，我们开发了一种广播方案，该方案能估计驾驶员意图，并将备用控制障碍函数与多种备用策略相结合用于人机交互。长短时记忆网络分类器利用来自机器人、人类和安全算法的数据输入，实时持续选择备用控制器。我们在双履带机器人的避障场景中验证了该方法的有效性。我们的框架在确保机器人安全性的同时，能够遵循驾驶员意图。