Implementation and Analysis of Shared-Control Guidance Paradigms for Improved Robot-Mediated Training

Many dynamic tasks have a clearly defined optimal trajectory or strategy for completion. Human operators may discover this strategy naturally through practice, but actively teaching it to them can increase their rate of performance improvement. Haptic devices, which provide force feedback to an operator, can physically guide participants through the optimal completion of a task, but this alone does not ensure that they will learn the optimal control strategy. In fact, participants may become dependent on this guidance to complete the task. This research focuses on developing and testing ways in which guidance can be modulated such that it conveys the proper task completion strategy without physically dominating the operator and thus encouraging dependency. These guidance schemes may also be applied to the real-time execution of tasks in order to convey computer-generated task completion strategies to a user without allowing the computer to physically dominate control of the task