Deciding on optimal assistance policies in haptic shared control tasks

This paper presents a haptic assistant that enhances task performance and human-machine interaction via a gain-scheduled impedance controller. The assistance strategy proposed builds on decision-making studies and models first proposed in the field of cognitive science and combines these models with a gain-scheduled impedance control technique in order to enhance human machine interaction in a tracking task with environmental uncertainties. This paper explores the Drift-Diffusion Model as decision making model and proposes an adaptive impedance control strategy that enhances both, task performance and human-machine interaction.