Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate complex environments, but developing certifiably safe feedback controllers around these sensors remains a challenging open problem, particularly when learning is involved. Previous works have proved the safety of perception-feedback controllers by separating the perception and control subsystems and making strong assumptions on the abilities of the perception subsystem. In this work, we introduce a novel learning-enabled perception-feedback hybrid controller, where we use Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) to show the safety and liveness of a full-stack perception-feedback controller. We use neural networks to learn a CBF and CLF for the full-stack system directly in the observation space of the robot, without the need to assume a separate perception-based state estimator. Our hybrid controller, called LOCUS (Learning-enabled Observation-feedback Control Using Switching), can safely navigate unknown environments, consistently reach its goal, and generalizes safely to environments outside of the training dataset. We demonstrate LOCUS in experiments both in simulation and in hardware, where it successfully navigates a changing environment using feedback from a Lidar sensor.
翻译:许多机器人任务需要高维传感器,如照相机和利达尔来导航复杂的环境,但是在这些传感器周围开发安全可靠的可靠反馈控制器仍然是一个挑战性的开放问题,特别是在涉及学习时。以前的工作通过将感知和控制子子系统分开,并对感知子系统的能力作出强有力的假设,证明了感知反馈控制器的安全性。在这项工作中,我们引入了一个创新的、以学习为动力的感知反馈混合控制器,在那里我们使用控制屏障功能(CBFs)和控制Lyapunov功能(CLyapunov功能)来安全地导航未知环境,不断达到目标,并安全地向培训数据集以外的环境推广。我们使用神经网络在机器人观察空间直接为全容系统学习CBF和CLF系统学习CF,而不必另设基于感知状态的估测器。我们称之为LOCUS的混合控制器,可以安全地导航未知环境,始终达到它的目标,并且能够安全地向培训数据集以外的环境推广。我们用LOCUS在模拟和硬件的感测器中进行实验,在模拟和感官方面成功地导航。