Using dynamic Bayesian optimization to induce desired effects in the presence of motor learning: a simulation study

We sought to establish whether dynamic Bayesian optimization (DBO) is a suitable algorithm for human-in-the-loop-optimization (HILO) of the control input of devices interacting with individuals whose output changes during optimization as resulting from motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming responses from state-space models of motor learning. DBO generally outperformed standard Bayesian optimization (BO) in convergence to optimal inputs and outputs after a certain number of iterations. DBO may improve the performance of HILO over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability and learning.