User-specific Gaussian Process Model of Wheelchair Drivers with a Haptic Joystick Interface

Abstract

In collaborative human-robot navigation such as when driving semi-autonomous robotic wheelchairs, intuitive control of the mobile robot is only possible if the robot understands its user. This becomes especially important as users present varying levels of abilities and heterogeneous driving styles. Furthermore, the robot needs to consider the inherent uncertainty on its navigation task because the user may not be able to communicate his or her plans explicitly. In order to address these requirements, we have adopted a probabilistic framework to recognise navigation plans. A key component in this framework is a personalised driver model, which captures how a particular user transforms his or her mental navigation plan into inputs to the robot. In this work, we evaluate the use of Gaussian Processes to implement and calibrate this probabilistic, user-specific driver model, and this for use with haptic joysticks. Furthermore, special care was taken to obtain fast online evaluation of this user model through sparse approximation and parallel computation on a GPU. This resulted in an achievable user model evaluation frequency of 40 Hz, which is far above the navigation assistance frequency we aimed for, i.e. 5 Hz. We illustrate the validity of the approach by recognising the navigation plans of a spastic wheelchair user.