Legible user input for intent prediction

In assistive teleoperation, the robot provides assistance by predicting the user´s intent. Prior work has focused on improving prediction by adapting it to the user´s behavior. In this work, we investigate adaptation in the opposite direction: training the user´s behavior to the prediction. Results from our user study suggest that users can significantly improve the performance of a simple static predictor after brief exposure to its behavior. In addition, we find this improvement to be more significant when the cognitive load of teleoperation is reduced.