A computational model of object affordances

摘要

The concept of object affordances describes the possible ways whereby an agent (either biological or artificial) can act upon an object. By observing the effects of actions on objects with certain properties, the agent can acquire an internal representation of the way the world functions with respect to its own motor and perceptual skills. Thus, affordances encode knowledge about the relationships between action and effects lying at the core of high-level cognitive skills such as planning, recognition, prediction and imitation. Humans learn and exploit object affordances through their entire lifespan, by either autonomous exploration of the world or social interaction. Building on a biological motivation and aiming at the development of adaptive robotic systems, we propose a computational model capable of encoding object affordances during exploratory learning trials. We represent this knowledge as a Bayesian network and rely on statistical learning and inference methods to generate and explore the network, efficiently dealing with uncertainty, redundancy and irrelevant information. The affordance model serves as base for an imitation learning framework, which exploits the recognition and planning capabilities to learn new tasks from demonstrations. We show the application of our model in a real-world task in which a humanoid robot interacts with objects, uses the acquired knowledge and learns from demonstrations. Results illustrate the success of our approach in learning object affordances and generating complex cognitive behavior.