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

• Published in: IEEE transactions on robotics Vol. 24; no. 1; pp. 15 - 26
• Main Authors: Montesano, L., Lopes, M., Bernardino, A., Santos-Victor, J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-02-2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affordances; Bayesian analysis; Bayesian methods; biorobotics; Buildings; Cognition & reasoning; Cognitive robotics; Games; Human robot interaction; Humanoid; Humanoid robots; Knowledge representation; Learning; Mathematical models; Motor ability; Networks; Probability theory; Robot kinematics; Robot vision systems; Robots; Sensory perception; Uncertainty
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2007.914848

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.