Experience Sharing and Human-in-the-Loop Optimization for Federated Robot Navigation Recommendation

Mobile robot navigation in unknown, dynamic, hostile and/or crowded environments is a challenging task, especially when it comes to multi-robot systems. Looking at the issue from the lens of human-robot interaction (HRI) and artificial general intelligence, the keys are enabling (mobile) robots to deal with such a problem as human beings do by learning from (themselves or) counterparts' experience and providing them with freedom of choice. Besides traditional solutions in the field, learning from historical knowledge, in the form of experience sharing and experience replay gained momentum within the recent years. Extending these notions, the idea of taking advantages of the robot's collected information and perception of the environment (and the obstacles within); previous (case-specific) decisions and their consequences as well as complementary information provided by human-in-the-loop optimization, such as human-generated suggestions and advice, to provide the robotic agent with context-aware navigation recommendations is introduced in this paper. More specifically, the conceptual architecture of a robot navigation recommender system (RoboRecSys) is proposed to provide the agent with several options (based on different criteria) for making more efficient decisions in finding the most appropriate path towards its goal. Moreover, in order to preserve the privacy of both agents' data and environmental perception information and their decisions (and feedback) based on the received recommendations, the federated learning approach is employed.