We designed an autonomous agent that discovers, learns, and exploits basic spatial regularities of interaction with its environment. To do so, we propose implementing a persistence memory system that records bundles of “possibilities of interaction” afforded by objects in the environment, coupled with a local space memory system that represents the agent’s surrounding local space (inspired by the vertebrate’s tectum). An experiment in a simple simulated environment demonstrates how the agent performs multimodal integration of sensory stimuli, and allocates the origin of such stimuli to “phenomena” located in the external spatial environment. Such mechanisms open the way to implementing agents with minimal preconception of their environment, and to modeling intrinsic motivation in autonomous agents.