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.