Learning of safety critical model predictive controllers for autonomous systems

Modern autonomous systems such as mobile robots and autonomous vehicles rely heavily on feedback controllers for motion control, particularly for path-following and obstacle avoidance, where they are employed to follow a trajectory set by a higher-level motion planner in a hierarchical control scheme. Model Predictive Control (MPC) is a popular controller choice for obstacle avoidance, as it allows constraints to be specified to ensure that the mobile robot or autonomous vehicle does not collide with obstacles. The behaviour of MPC is well understood from years of theoretical development and industrial practice, providing strong safety assurances, but considerable time and expert knowledge is required to implement it, especially in safety-critical applications such as autonomous vehicles.

In recent years, research on deep Reinforcement Learning (RL) has provided new methods to automatically find nonlinear feedback controllers for challenging control problems. But unlike MPC, existing RL methods typically have no guarantees of stability or of constraint satisfaction, and for safety-critical applications it is difficult to verify their behaviour.

To combine the predictability and safety guarantees of MPC with the power and convenience of modern RL methods, this project will develop methods to automatically learn MPC controllers in actor-critic RL frameworks, considering motion control and obstacle avoidance problems for autonomous vehicles. This will be a direct application of recent mathematical results showing that convex optimisations, such as MPC, can be employed as a trainable layer in RL frameworks such as PyTorch, allowing them to be learned. The goal is to enable rapid design and prototyping of path-following type MPC without requiring expert-knowledge of the underlying MPC algorithm, therefore reducing development time and cost and improving safety and reliability of future mobile robots and autonomous vehicles.

To ensure the new algorithms are practically applicable, an example application of motorcycle path-following and stability assistance will be used to guide their development. The problem of stabilising a two-wheeled vehicle in forward motion to follow a predefined path, for example via steering actuation, is challenging and has important applications in the emerging area of active safety systems for motorcycles and scooters.

For long term impact and to encourage adoption of the new methods by autonomous systems researchers, the new methods developed will be included in an open-source software library published on Github.