Learning to Control a Three-Dimensional Ferrofluidic Robot

In recent years, ferrofluids have found increased popularity as a material for medical applications, such as ocular surgery, gastrointestinal surgery, and cancer treatment, among others. Ferrofluidic robots are multifunctional and scalable, exhibit fluid properties, and can be controlled remotely; thus, they are particularly advantageous for such medical tasks. Previously, ferrofluidic robot control has been achieved via the manipulation of handheld permanent magnets or in current-controlled electromagnetic fields resulting in two-dimensional position and shape control and three-dimensional (3D) coupled position–shape or position-only control. Control of ferrofluidic liquid droplet robots poses a unique challenge where model-based control has been shown to be computationally limiting. Thus, in this study, a model-free control method is chosen, and it is shown that the task of learning optimal control parameters for ferrofluidic robot control can be performed using machine learning. Particularly, we explore the use of Bayesian optimization to find optimal controller parameters for 3D pose control of a ferrofluid droplet: its centroid position, stretch direction, and stretch radius.