Learning Object Affordances: From Sensory--Motor Coordination to Imitation

TLDR

Affordances encode relationships between actions, objects, and effects, and they are essential for basic cognitive capabilities such as prediction and planning. The study aims to learn affordances through robot interaction with the environment, a key step toward understanding world properties and developing social skills. The authors propose a general Bayesian network model integrated into a developmental architecture for social robots, enabling learning under uncertainty, redundancy, and irrelevant information. Experiments with a humanoid robot in real‑world settings show successful affordance learning and demonstrate the benefits of the acquired knowledge in imitation games.

Abstract

Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.