This seminar was concerned with answering the question: how should a robot choose its actions and experiences so as to maximise the effectiveness of its learning?.

This seminar was predicated on the assumption that to make significant progress in autonomous robotics, systems level theories of how robots should operate will be required. In recent years methods from machine learning have been employed with great success in robotics, in fields as diverse as visual processing, map building, motor control and manipulation. The machine learning algorithms applied to these problems have included statistical machine learning approaches, such as EM algorithms and density estimation, as well as dimensionality reduction, reinforcement learning, inductive logic programming, and other supervised learning approaches such as locally weighted regression. Most of these robot learning solutions currently require a good deal of supervised learning, or structuring of the learning data for a specific task. As robots become more autonomous these learning algorithms will have to be embedded in algorithms which choose the robot's next learning experience. The problems become particularly challenging in the context of robotics, and even worse for a robot that is faced with many learning opportunities. A robot that can perform both manipulation, language use, visual learning, and mapping may have several quite different learning opportunities facing it at any one time. How should a robot control its curiosity in a principled way in the face of such a variety of choices? How should it choose data on the basis of how much it knows, or on how surprising it finds certain observations? What rational basis should robot designers choose to guide the robot's choice of experiences to learn from? There has been initial progress in several fields, including machine learning, robotics and also in computational neuroscience. In this seminar we brought together these three communities to shed light on the problem of how a robot should select data to learn from, how it should explore its environment, and how it should control curiosity when faced with many learning opportunities.