In most of the previous century, the majority of robots were performing the same manufacturing task again and again in extremely structured environments such as automobile factories. Everything could be envisioned in advance and the achievement of the task could be pre-programmed by the designer. By contrast, the personal robots that represent the future of robotics will have to evolve in unpredictable environments such as homes and streets, they will have to achieve a large variety of tasks and to adapt to the needs of very different users. In this new context, programming in advance the behaviour of the robot to achieve any task in any context is not a viable approach anymore. An obvious alternative to behaviour programming at the design stage consists in endowing the robot with some learning capabilities that will let it adapt its behaviour on the fly to experienced circumstances. With this goal in mind, research in artificial intelligence, machine learning and pattern recognition has produced a tremendous amount of results and concepts in the last decades. A blooming number of learning paradigms – supervised, unsupervised, reinforcement, active, associative, symbolic, neural, situated, hybrid, distributed ...- nourished the elaboration of highly sophisticated algorithms for robotics capabilities such as visual object recognition, speech recognition, robot walking, grasping or navigation, etc. Yet, we are still very far from being able to build robots capable of adapting to the physical and social environment with the flexibility, robustness, and versatility of a one-year-old human child. Developmental (or epigenetic) robotics is an approach to robotics that takes inspiration from developmental psychology and tries to endow a robot with the above properties taking inspiration from the developmental processes that take place in children. Framed into this research agenda, our approach aims at importing some of the principles of infant sensorimotor development into machines to build mechanisms that allow a robot to efficiently learn new basic sensorimotor skills driven by its own motivations as well as by social incentives and feedback from a guiding human. The challenge is to build robots that possess the capability to discover, adapt and develop continuously new skills and new knowledge in unknown and changing environments, like human children do. More precisely, the central target of the MACSi project is to build mechanisms that allow a robot to efficiently develop new basic sensorimotor skills through both autonomous exploration and social interaction with humans in partially unknown environments. Our approach will consist in designing a set of well identified core capabilities and learning mechanisms that will provide a good starting point onto which more complex capabilities can be developed in the future. In pratice we will realize a scenario where the iCub robot is seated at a table with a few objects within reach. The robot will typically perform organized motor babbling and explore what it can do with its hands and with objects. A human caregiver will sometimes be in the front of the robot, giving feedback on the robot behaviour and attracting the robot’s attention toward particular objects (for example by shaking the object). From these interactions, the robot is expected to build increasingly complex representations of the surrounding world, giving rise to the edification of basic affordances.