Issues, algorithms, and challenges

Learning appears as one of the most fascinating aspects of cognition and as a hallmark of intelligence -- be it natural or artificial. However, learning as seen in cognitive agents that must act and survive in natural environments is often far from the crisp and idealized notions of learning that have become elaborated in machine learning. Here, the major dichotomy is to identify a mapping or a probability density, with a plethora of methods to represent, construct and evaluate these "objects" leading to a meanwhile richly differentiated spectrum of learning algorithms and their characterization.

Robotics exerts an increasing impact on this science by introducing a number of challenges that tended to by sidestepped in earlier machine learning work that circled mainly around the idea of "mappings" or probability densities to be estimated from large numbers of passively observed examples. First of all, robots with their closed sensory-motor loops require to consider learning when the data are not only observed but also changed or even created during acting, which necessitates a perspective shift from mappings to controllers or dynamical systems. Moreover, even our simplest daily actions connect different levels of learning that seem to coexist in real-world "cognitive" learners with their embarrassingly strong generalization abilities that enable them to learn from much fewer examples than most current machine learning algorithms would require. Finally, the grounding of robots in the real world that results from their sensors, actors and their body shape, is far from trivial and has a strong impact on what can be learnt and what needs to be learnt.

These and more factors make the interface between robotics and the fields of machine and of cognitive learning a rich area with exciting developments, scientific challenges and opportunities for research. The tutorial attempts a (highly selective) "tour" through some of the major issues with a discussion of the roles and lessons from robotics as a "connecting science" between the strands of machine learning and cognitive learning research. Along this way, we show and discuss examples from pertinent current work, including a status report and outlook on the recently launched project FAMULA that connects a number of CITEC research groups for developing a robot that employs manual action and language to familiarize itself with novel objects.