Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Abstract

Energy-function-based safety certificates can provide demonstrable safety for complex automatic control systems used in safety control tasks. However, recent studies on learning-based energy function synthesis have only focused on feasibility, which can lead to over-conservatism and reduce controller efficiency. In this study, we propose using magnitude regularization techniques to enhance the efficiency of safe controllers by reducing conservativeness within the energy function while maintaining promising demonstrable safety guarantees. Specifically, we measure conservativeness by the magnitude of the energy function and reduce it by adding a magnitude regularization term to the integrated loss. We present the SafeMR algorithm, which is synthesized using reinforcement learning (RL) to unify the learning process of the safety controller and the energy function. To verify the effectiveness of the algorithm, we conducted two sets of experiments, one in a robot-based environment and the other in an autonomous vehicle environment. The experimental results demonstrate that the proposed approach reduces the conservativeness of the energy function and outperforms the baseline in terms of controller efficiency for the robot, while ensuring safety.

Keywords

• Autonomous vehicles
• safety and security
• machine learning
• automotive and transportation safety
• robot control