Sequential Action Selection for Budgeted Localization in Robots

Recent years have seen a fast growth in the number of applications of Machine Learning algorithms from Computer Science to Robotics. Nevertheless, while most such attempts were successful in maximizing robot performance after a long learning phase, to our knowledge none of them explicitly takes into account the budget in the algorithm evaluation: e.g. budget limitation on the learning duration or on the maximum number of possible actions by the robot. In this paper we introduce an algorithm for robot spatial localization based on image classification using a sequential budgeted learning framework. This aims to allow the learning of policies under an explicit budget. In this case our model uses a constraint on the number of actions that can be used by the robot. We apply this algorithm to a localization problem on a simulated environment. Our approach enables to reduce the problem to a classification task under budget constraint. The model has been compared, on the one hand, to simple neural networks for the classification part and, on the other hand, to different techniques of policy selection. The results show that the model can effectively learn an efficient policy (i.e. alternating between sensor measurement and movement to get additional information in different positions) in order to optimize its localization performance under each tested fixed budget.