At home, workplaces or schools, an increasing amount of intelligent robotic systems are starting to be able to help us in our daily life (windows or vacuum cleaners, self-driving cars) [1] and in flexible manufacturing systems [2]. A key feature in these new domains is the close interaction between people and robots. In particular, such robotic systems need to be teachable by non-technical users, i.e. programmable for new tasks in novel environments through intuitive, flexible and personalized interactions. Specifically, the challenge we address in this work is how a robot learning a new task can be instructed by a human using its own preferred teaching signals, where the mapping between these signals and their meaning is unknown to the robot. For example, different users may use different languages, words, interjections, gestures or even brain signals to mean "good" or "bad".