Composite Reinforcement Learning for Social Robot Navigation

For a service robot, it is not adequate to let its navigational movement be based only on a single metric, such as minimum distance path. In the environment where the robot and humans are coexisting, the robot should always perform social navigation whenever it is moving. However, to perform social navigation, the robot needs to follow certain "social norms" of the environment. Recently, deep reinforcement learning (DRL) technique is popularly applied to the robotics field; yet, it is rarely used to solve the mentioned social navigation problem, generally deemed as a high dimension complex problem. In this paper, we propose the composite reinforcement learning (CRL) framework under which the robot learns appropriate social navigation with sensor input and reward update based on human feedback. For learning the aspect of human robot interaction (HRI), we provide a method to facilitate the training of DRL in real environment by incorporating prior knowledge to the system. It turns out that our CRL system not only can incrementally learn how to set its velocity and to perform HRI but also keep collecting human feedback to synchronize the reward functions to the current social norms. The experiments show that the proposed CRL system can safely learn how to navigate in the environment and show that our system is able to perform HRI for social navigation.