Learning Object Affordances for Tool Use and Problem Solving in Cognitive Robots

One of the hallmarks of human intelligence is the ability of predicting the consequences of actions and efficiently plan behaviors based on such predictions. This ability is supported by internal models that human babies acquire incrementally during development through sensorimotor experience: i.e. by interacting with objects in the environment while being exposed to sensori perception. An elegant and powerful concept to represent these internal models has been proposed in developmental psychology under the name of object affordances: action possibilities that an object offers to an agent. Affordances are learned ecologically by the agent and exploited for action planning. Clearly, endowing artificial agents with such cognitive capabilities is a fundamental challenge both in artificial intelligence and robotics. We propose a learning framework in which an embodied agent (i.e. in our case, the humanoid robot iCub) autonomously explores the environment, and learns object affordances as probabilistic dependencies between actions, object visual properties and observed effects; we use Bayesian Networks to encode this probabilistic model. By making inferences across the learned dependencies a number of cognitive skills are enabled: e.g. i) predicting the effects of an action over an object, or ii) selecting the best action to obtain a desired effect. By exploring object-object interactions the robot can develop the concept of tool (i.e. a handheld object that allows to obtain a desired effect on another object), and eventually use the acquired knowledge to plan sequences of actions to attain a desired goal (i.e. problem solving).