The challenges posed by robots operating in human environments on a daily basis and in the long-termpoint out the importance of adaptivity to changes which can be unforeseen at design time. The robot mustlearn continuously in an open-ended, non-stationary and high dimensional space. It must be able to knowwhich parts to sample and what kind of skills are interesting to learn. One way is to decide what to exploreby oneself. Another way is to refer to a mentor. We name these two ways of collecting data sampling modes.The first sampling mode correspond to algorithms developed in the literature in order to autonomously drivethe robot in interesting parts of the environment or useful kinds of skills. Such algorithms are called artificialcuriosity or intrinsic motivation algorithms. The second sampling mode correspond to social guidance orimitation where the teacher indicates where to explore as well as where not to explore. Starting fromthe study of the relationships between these two concurrent methods, we ended up building an algorithmicarchitecture with a hierarchical learning structure, called Socially Guided Intrinsic Motivation (SGIM).We have built an intrinsically motivated active learner which learns how its actions can produce variedconsequences or outcomes. It actively learns online by sampling data which it chooses by using severalsampling modes. On the meta-level, it actively learns which data collection strategy is most efficient forimproving its competence and generalising from its experience to a wide variety of outcomes. The interactivelearner thus learns multiple tasks in a structured manner, discovering by itself developmental sequences.
