We propose a learning mechanism that allows an artificial agent to construct and exploit a representation of its surrounding space with minimal preconceptions about its environment. This representation is based on a data structure that encodes possibilities of behaviors afforded by the current context. The behaviors are modeled in the form of sequences of interactions. Over time, the agent learns to associate sequences of interactions with the presence of certain elements of the environment in certain locations in the agent's surrounding space. The agent uses this emergent relation between objects and possibilities of interactions to construct and maintain a representation of the surrounding space based on sequences of interactions. Experiments show that efficiently learning object and interaction associations requires implementing a form of curiosity as an additional motivational principle of the agent. These mechanisms open the way to implementing agents that learn to generate and exploit an awareness of their surrounding space with a minimal preconception of their environment.