Learning Safe, Generalizable Perception-based Hybrid Control with Certificates
This addresses the challenge of ensuring safety in robotic perception-based control for tasks like navigation, though it appears incremental by building on existing methods like CBFs and CLFs with learning enhancements.
The paper tackled the problem of developing certifiably safe feedback controllers for robots using high-dimensional sensors like cameras and Lidar by introducing a learning-enabled perception-feedback hybrid controller called LOCUS, which demonstrated safe navigation and goal-reaching in unknown environments with generalization to unseen scenarios in simulation and hardware experiments.
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.