Learning the MPC objective function from human preferences

Abstract

In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the desired closed-loop performance. However, in real-world scenarios, its design is often challenging, as it requires balancing complex trade-offs and accurately capturing a performance criterion that may not be easily quantifiable in terms of an objective function. This paper explores preference-based learning as a data-driven approach to constructing an objective function from human preferences over trajectory pairs. We formulate the learning problem as a machine learning classification task to learn a surrogate model that estimates the likelihood of a trajectory being preferred over another. The approach provides a surrogate model that can directly be used as an MPC objective function. Numerical results show that we can learn objective functions that provide closed-loop trajectories that align with the expressed human preferences.