Abstract-In this paper, a robot learning approach is proposed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of performed actions is learned through demonstrations using Visuospatial Skill Learning. A standard action-level planner is used to represent a symbolic description of the skill, which allows the system to represent the skill in a discrete, symbolic form. The Visuospatial Skill Learning module identifies the underlying constraints of the task and extracts symbolic predicates (i.e., action preconditions and effects), thereby updating the planner representation while the skills are being learned. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be planned and performed independently during the learning phase. Preliminary experimental results on the iCub robot are presented.

Human expertise in face perception grows over development, but even within minutes of birth, infants exhibit an extraordinary sensitivity to face-like stimuli. The dominant theory accounts for innate face detection by proposing that the neonate brain contains an innate face detection device, dubbed 'Conspec'. Newborn face preference has been promoted as some of the strongest evidence for innate knowledge, and forms a canonical stage for the modern form of the nature-nurture debate in psychology. Interpretation of newborn face preference results has concentrated on monocular stimulus properties, with little mention or focused investigation of potential binocular involvement. However, the question of whether and how newborns integrate the binocular visual streams bears directly on the generation of observable visual preferences. In this theoretical paper, we employ a synthetic approach utilizing robotic and computational models to draw together the threads of binocular integration and face preference in newborns, and demonstrate cases where the former may explain the latter. We suggest that a system-level view considering the binocular embodiment of newborn vision may offer a mutually satisfying resolution to some long-running arguments in the polarizing debate surrounding the existence and causal structure of newborns' 'innate knowledge' of faces.

The complexity of humanoid robots is increasing with the availability of new sensors, embedded CPUs, and actuators. This wealth of technologies allows researchers to investigate new problems like multi-modal sensory fusion, whole-body control and multimodal human-robot interaction. Under the hood of these robots, the software architecture has an important role: it allows researchers to get access to the robot functionalities focusing primarily on their research problems and supports code reuse to minimize development and debugging, especially when new hardware becomes available. But more importantly, it allows increasing the complexity of the experiments that can be carried out before system integration becomes unmanageable, and debugging draws more resources than research itself. In this paper, we illustrate the software architecture of the iCub humanoid robot and the software engineering best practices that have emerged driven by the needs of our research community. We describe the latest development of the middleware supporting interface definition and automatic code generation, logging, ROS compatibility, and channel prioritization. We show the robot abstraction layer and how it has been modified to better address the requirements of the users and to support new hardware as it became available. We also describe the testing framework, and we have recently adopted for developing code using a test-driven methodology. We conclude the paper discussing the lessons we learned during the past 11 years of software development on the iCub humanoid robot.

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