The ability to adapt to changing environment autonomously will be essential for future robots. While this need is well-recognized, most machine learning research focuses largely on perception and static data sets. Instead, future robots need to interact with the environment to generate the data that is needed to foster real-time adaptation based on all information collected in previous interactions and observations. In other words, we need to close the loop between the robot acting, robot sensing and robot learning. Novel active methods need to outperform passive methods by a margin that compensates the potential of the extra computational burden and the cost of the active data sampling.
In this talk, we present a common framework for active learning in different applications, such as planning, robot localization and mapping, calibration, sensor networks and computer graphics. Our results show that in many applications, active sampling provides an improvement, while in other applications is mandatory to achieve a good performance.