Identifying and localizing dynamic affordances to improve interactions with other agents

Allowing robots to learn by themselves to coordinate their actions and cooperate requires that they be able to recognize each other and be capable of intersubjectivity. To comply with artificial developmental learning and self motivation, we follow the radical interactionism hypothesis, in which an agent has no a priori knowledge on its environment (not even that the environment is 2D), and does not receive rewards defined as a direct function of the environment’s state. We aim at designing agents that learn to efficiently interact with other entities that may be static or may make irregular moves following their own motivation. This paper presents new mechanisms to identify and localize such mobile entities. The agent has to learn the relation between its perception of mobile entities and the interactions that they afford. These relations are recorded under the form of data structures, called signatures of interaction, that characterize entities in the agent’s point of view, and whose properties are exploited to interact with distant entities. These mechanisms were tested in a simulated prey-predator environment. The obtained signatures showed that the predator successfully learned to identify mobile preys and their probabilistic moves, and to efficiently localize distant preys in the 2D environment.