The acquisition of intentionally indexed and object centered affordance gradients: A biomimetic controller and mobile robotics benchmark

We introduce affordance gradients (AGs), continuous sensorimotor structures that allow to predict the consequences of the agent's actions on the state of the environment. AGs allow to generalize among never performed actions and compress all possible consequences of the action state space. AGs also provide a way of estimating the world state after several interactions of the agent with objects. We validate the notion of AGs using benchmarks designed for mobile robotics that we solve using E-puck robot simulations: learn the affordances of several objects, push an object along a predefined trajectory and place an object in at a target position and orientation. We are interested in the neurophysiological basis of affordances and how they can be inserted in a sensorimotor loop with memory structures like the one proposed by the DAC architecture. We show that AGs provide a generalization of the perception-action couplets stored in memory and learned by they adaptive layer of DAC.