Learning user habits for semi-autonomous navigation using low throughput interfaces

This paper presents a semi-autonomous navigation strategy aimed at the control of assistive devices (e.g. an intelligent wheelchair) using low throughput interfaces. A mobile robot proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In case of rejection, the robot will propose another action, until both entities agree on what needs to be done. In a known environment, the system infers the intended goal destination based on the first executed actions. Furthermore, we endowed the system with learning capabilities, so as to learn the user habits depending on contextual information (e.g. time of the day or if a phone rings). This additional knowledge allows the robot to anticipate the user intention and propose appropriate actions, or goal destinations.