Neural Certificates for Safe Control Policies
It addresses safety and stability in control systems for robotics and autonomous systems, representing an incremental advance by combining existing certificate concepts with neural networks.
The paper tackles the problem of learning control policies for dynamical systems that are both provably safe and goal-reaching by jointly learning neural barrier and Lyapunov-like certificate functions, demonstrating effectiveness on systems like pendulums, cart-poles, and UAVs.
This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching. Here, the safety means that a policy must not drive the state of the system to any unsafe region, while the goal-reaching requires the trajectory of the controlled system asymptotically converges to a goal region (a generalization of stability). We obtain the safe and goal-reaching policy by jointly learning two additional certificate functions: a barrier function that guarantees the safety and a developed Lyapunov-like function to fulfill the goal-reaching requirement, both of which are represented by neural networks. We show the effectiveness of the method to learn both safe and goal-reaching policies on various systems, including pendulums, cart-poles, and UAVs.