@misc {PUBA299,
	title = {Developmental Integration of Sensorimotor Mappings},
	author = {Nikolas Hemion AND Frank Joublin AND Katharina Rohlfing},
	year = {2013},
	month = {August},
	abstract = {Sensorimotor mappings, such as kinematic transformations or control policies, are essential tools for robots to solve tasks. In the developmental robotics and machine learning literature, the question of how robots can learn sensorimotor mappings from own experiences is often investigated, enabling robots for example to learn to reach for objects, to balance poles or to swing tennis rackets. However, the important problem of how multiple sensorimotor mappings can be integrated into a single system in a developmental way is rarely addressed. Instead, either only a single sensorimotor mapping is used, and thus the robot{\textquoteright}s function is limited to a single task, or traditional symbolic methods are employed that have several limitations and require the designer to provide additional task-specific knowledge, and thus cannot be employed by the robot autonomously. This poster discusses the question of how sensorimotor mappings can be integrated generically in a robotic system. We argue that this issue should also guide the choice and the design of learning methods and representations. Specifically, we propose that redundancies in sensorimotor tasks can be usefully exploited for the purpose of integration, and thus that learning methods should be able to retain information about redundancies. These considerations lead us towards the design of a generic building block, which could be the basis for a developmentally plausible cognitive architecture.},
	publisher = {Marie Curie doctoral training network},
	booktitle = {ROBOTDOC International Conference on Development of Cognition},
	city = {Playmouth}
}