Section: New Results
Learning the informational topology in robotic body maps.
In developmental robotics  , one aims at building robot capable of learning progressively and continuously new skills in unknown changing bodies and environments. In particular, this involves mechanisms for discovering its own body and its relationships with the environment. In this context, learning body maps is a crucial challenge. Body maps are topological models of the relationships among body sensors and effectors, which human children learn progressively, abstract and build upon to learn higher-level skills involving the relationships between the shape of the body and the physical environment  . Accordingly, inferring and re-using body maps from initially uninterpreted sensors and effectors has been identified as an important objective in developmental robotics  . Several authors  have proposed an approach based on information theory and dimensionality reduction to infer the topology of the body. Based on this approach, we have extended the method to allow its applicability to dynamically reconfigurable bodies, and proposed extensions that allow to re-use these body maps to control reconfigurable bodies. This work is described in  .
Further technical publications are under way.