Model Predictive Control with Graph Dynamics for Garment Opening Insertion during Robot-Assisted Dressing

Robots have a great potential to help people with movement limitations in activities of daily living, such as dressing. A common problem in almost all dressing tasks is the insertion of a garment’s opening around a part of the human body. The rich contact environment and the deformations of the garment make the task a challenging problem for robots. In this paper, we propose a bi-manual control method for garment opening insertion during robot-assisted dressing. Specifically, we propose a model predictive controller that uses an Attention-based Relational Graph Convolutional Network (ARGCN) for modeling the dynamics of the opening in the presence of the body. We train the model entirely in simulation and validate our method in four real-world dressing scenarios of a medical training manikin. We show that our method generalizes well in the real-world opening insertion tasks achieving an overall success rate of 97.5%, even though the dynamics and the shapes vastly differ from the simulation setup.