Constrained Scheduling of Robot Exploration Tasks

n order for an autonomous service robot to provide the best service possible for its users, it must have a considerable amount of knowledge of the environment in which it operates. Service robots perform requests for users and can learn information about their environment while performing these requests. We note that such requests do not take all of the robot's time, and propose that a robot could schedule additional exploration tasks in its spare time to gather data about its environment. The data gathered can then be used to model the environment, and the model can be used to improve the services provided. Such additional exploration is a constrained form of active learning, in which the robot evaluates the knowledge it can acquire and chooses observations to gather, while being constrained by its navigation and the time underlying the user requests it receives. We create a schedule of exploration tasks that meets the given constraints using a hill-climbing approach on a graph of tasks the robot can perform to gather observations. We test this method in simulation of a CoBot robot and find that is able to learn an accurate model of its environment over time, leading to a near-optimal policy when scheduling user requests.