A Cooperative Assistant System with Smoothly Shifting Control Authority Based on Partially Observable Markov Decision Processes

In order to support a human in a human-machine system, the cooperating automation requires information about the goal pursued by the human. We model human-machine systems as a Partially Observable Markov Decision Process to develop an assistant system operating on maneuver or navigation level featuring an automatic detection of the humans goal which is initially unknown to it. Predicting the next actions of the human allows for computing corresponding assistant actions by employing Partially Observable Monte-Carlo Planning With Observation Widening. These supporting actions are generated based on continuous action-, observation- and state spaces and are executed synchronously to the actions of the human. New information about the humans goal is gathered through observations to further enhance future supporting actions. With a progressing certainty of the goal recognition, the assistant system is increasingly able to assist the human by completing the task both cooperatively or even fully autonomously, thus reducing workload, while always allowing for the human to smoothly in-or decrease their involvement. The assistant system demonstrates its ability to recognize the goal pursued by the human as well as to execute appropriate supporting actions while being robust to the human changing their goal in multiple experiments investigating the interaction of a real human with a simulated cooperative positioning scenario.