We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for be- havior planning. State representations are learned with a growing self- organizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lat- eral connectivity. Motor signals modulate this lateral connectivity and a dynamic ﬁeld on the layer organizes a planning process. All mecha- nisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.