Today's robotic systems are given increasingly complex tasks in an increasing variety of situations such as object or social interaction. Many of those situations cannot be anticipated at design time : autonomous learning capacities are needed to adapt to novel, unexpected conditions. Yet, because of their complex bodies and multiple sensors, robots face highly-dimensional, unbounded, continuous sensorimotor spaces whose semantics are often unknown. Such spaces are too large to be explored exhaustively, an issue even more crucial in robotics given the expensive and slow nature of the physical interactions needed to gather training data. Learning in those spaces also raises other challenges, because robot's sensorimotors spaces are highly heterogeneous and multi-modal, with unreachable areas because of physical constraints, unlearnable areas because the actions of the agent do not have any influence on the sensors values, and yet other area where learning is made difficult by huge noise-to-signal ratios or requires the previous aquisition of other skills (e.g. learning reaching before grasping). This is why efficient explorations techniques are needed, where each interaction maximize the knowledge or competence gained through each interaction. To adress this issue, statistical learning techniques have focused on optimizing exploration policies to maximize various criteria in particular through active learning [1]-[3]. Another approach have stemmed from the field of developmental robotics, where inspiration from psychology and neuroscience research on animal and infant learning [4] [5] [6] have highlighted the importance of curiosity in skill acquisition. Several intrinsically motivated learning techniques have been proposed [10] [11] [12]. In this article, we will build on a particular intrinsically motivated, goal-oriented technique initiated by Baranes and Oudeyer [7], which defines the interest of an area of the sensorimotor space as the progress of the competence in reaching self-assigned goals in this area. This method has yielded excellent results in experiment with motor spaces of high dimension. Yet sensory spaces have remained limited to 2 or 3 dimensions, and the robot had only one type of action to consider. Moreover, the goal space was predefined by hand. We propose a broad expansion of the previous architecture, where the sensory space has 10+ dimensions, and relevant goal space are created and their interest evaluated by the algorithms through novel techniques. Additionally, we considers robotic agents that have several different actions at their disposal that can combine them temporally. To our knowledge, no existing work addresses both those challenges.