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：在复杂环境中（如受力矩限制等驱动约束下）确保机器人安全是一项艰巨任务。本文提出一种安全关键控制框架，通过基于学习的多个备份控制器之间切换，在满足驾驶意图的同时，形式化保证有界控制输入下的安全性。利用为维护安全与输入约束所设计的备份控制器，备份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，备份控制障碍函数的性能高度依赖于所选备份控制器的设计与保守性，尤其是在复杂（如越野）环境下的人驾驶车辆场景。虽然采用多个备份控制器可降低保守性，但切换时机的确定仍是一个开放问题。为此，我们开发了一种广播机制来估计驾驶意图，并将备份控制障碍函数与多备份策略相结合以应对人机交互。长短时记忆分类器持续接收来自机器人、人类和安全算法的数据输入，实时选择备份控制器。我们通过在障碍物避让场景中的双轨迹机器人实验验证了该方法有效性。该框架在遵循驾驶意图的同时保障了机器人安全性。