Towards real-time haptic assistance adaptation optimizing task performance and human effort

Abstract

In a haptic shared control system, a virtual assistant and a human share the control over performed actions to facilitate execution of manipulation tasks. The assistance level determines the amount of support provided by the assistant. It should be adapted autonomously such that task performance and human effort are optimized. The effect of the assistance level on task performance and human effort may, however, be different depending on whether human and assistant agree on the actions or not. In this paper, we investigate the effect of the assistance level on task performance and effort for a scenario, in which human and assistant agree and for a scenario, where they disagree. We present a force-based criterion for distinguishing between the two scenarios and introduce an approach to optimize the assistance levels for each of the scenarios. Finally we sketch, how the results can be used to develop novel assistance adaptation schemes.