The Recipe for Adapting

Analogous to the oven, a controller is a mechanism that continuously adjusts a system’s behavior to achieve a specific task. It lies at the heart of several technologies, ranging from automated industrial machines to robotic manipulators. These controllers operate based on tuned parameters – settings which influence the outcome and performance of the system. Consider a milling machine that handles a range of materials from aluminum to hardened steel. The output speed and pressure of the machine are affected by the choice of the controller’s weighting gains. Therefore, these internal parameters must be recalibrated to maintain optimal precision and quality of the final product for each material, respectively. In the literature, the optimal parameters for each task are retrieved via an iterative parameter update process. This approach is computationally intensive, time-consuming and therefore impractical for unforeseen scenarios encountered during operation.

Hence, although some tasks come with precise settings through well-established methods, others such as unfamiliar alloys require the system to intuitively adapt. Our research focuses on the use of meta-learning, a framework that enables controllers to automatically update their parameters for new tasks quickly and effectively by drawing on previously tuned parameters, just as a skilled chef instinctively adjusts the oven settings to optimize for novel recipes. Therefore, by embedding this fast adaptive technique in control systems, we enable them to optimally respond to changing environments, seamlessly and efficiently.