Autonomous Object Modeling based on Affordances in a Dynamic Environment

We present an architecture for self-motivated agents to generate behaviors in a dynamic envi- ronment according to its possibilities of interactions. Some interactions have predefined valences that specify inborn behavioral preferences. Over time, the agent learns to recognize affordances in its surrounding environment under the form of structures called signatures of interactions. The agent keeps track of enacted interactions in a spatial memory to generate a completed con- text in which it can use signatures to recognize and localize distant possibilities of interactions, and generates behaviors that satisfy its motivation principles.