Training with haptic shared control to learn a slow dynamic system

During operation of slow dynamic systems such as heavy machinery, users must account for inherent lag in the system dynamics, often via the less intuitive rate control mode. The slow response of these systems requires predictive control based on an understanding of the input-output relationship of system dynamics. In practical applications, such as learning to control an excavator, training can be a long and therefore costly process. This paper investigates the use of haptic shared control (HSC) to support learning of a system with slow dynamics. Previous work has failed to reach a consensus on the effectiveness of training with HSC, although a few recent studies have demonstrated improvements in tasks with time-critical components. Here, subjects learned to perform a pursuit task while controlling a linear system with slow dynamics using a 1-DOF haptic manipulator, either with or without HSC during training. To prevent reliance on the guidance forces, HSC was only present on intermittent trials and decreased in strength over time. Both groups quickly learned the task and showed similar performance after training, regardless of whether or not they trained with HSC.