Rebellion and Obedience: The Effects of Intention Prediction in Cooperative Handheld Robots

Abstract

Within this work, we explore intention inference for user actions in the context of a handheld robot setup. Handheld robots share the shape and properties of handheld tools while being able to process task information and aid manipulation. Here, we propose an intention prediction model to enhance cooperative task solving. The model derives intention from the combined information about the user's gaze pattern and task knowledge. Within experimental studies, the model is validated through a comparison of user frustration for the case where the robot follows the predicted location of the user's intended action versus doing the opposite (rebellion). The proposed model yields real-time capabilities and reliable accuracy up to 1.5 s prior to predicted actions being executed.