A novel formalization for robot cognition based on Affordance model

Affordance encodes the latent “action possibilities” for a given robot to interact with the environment. In this paper, we first present a 4-tuple formalization to describe the robot and environment systematically, in which precondition and postcondition could enable each action to take place in a measureable way. Analysis functions extract functional information from the environment, and they are the basis of our formalization. Then, the key problem of Affordance learning is addressed based on analysis functions, and the robot control architecture is also presented. In the simulation experiment, the robot performed the task effectively under our framework.