Affordances as a Framework for Robot Control

The concept of affordances, with its emphasis on the interaction between the organism and the environment, is highly relevant for robotics. In this paper, we present a new formalization of affordances that provides a framework for robot control, learning and planning. We argue that affordances, as relations within the robotenvironment system, can be learned by the robot from its interactions with the environment. The interaction is represented as a nested triple of the form (effect, (entity, behavior)) indicating that the behavior applied in an environment perceived as the entity, would produce a perceivable effect. The robot accumulates such triples of raw sensory-motor data from different interactions and learns the affordance relations through the formation of equivalence classes. We present three studies that implement certain aspects of the formalism on a mobile robot moving in an environment that contains different types of objects. These studies respectively explore (1) the perceptual learning of affordances, (2) the development of goal-directed behaviors from a set of primitive ones through the learning of affordances, and (3) the use of learned affordance relations in planning.

[1]  Manish Kumar,et al.  Visual Learning of Affordance Based Cues , 2006, SAB.

[2]  E. Gibson.  Perceptual Learning in Development: Some Basic Concepts , 2000 .

[3]  E. Gibson.  The World Is So Full of a Number of Things: On Specification and Perceptual Learning , 2003 .

[4]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[5]  Maya Cakmak,et al.  To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control , 2007, Adapt. Behav..

[6]  E. Sahin,et al.  Curiosity-driven learning of traversability affordance on a mobile robot , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[7]  Gillian M. Hayes,et al.  Motivation-driven learning of object a ordances: First experiments using a simulated khepera robot , 2003 .

[8]  Giulio Sandini,et al.  Developmental robotics: a survey , 2003, Connect. Sci..

[9]  A. Chemero.  An Outline of a Theory of Affordances , 2003, How Shall Affordances be Refined? Four Perspectives.

[10]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[11]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[12]  Alexander Stoytchev,et al.  Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[13]  Maya Cakmak,et al.  Using learned affordances for robotic behavior development , 2008, 2008 IEEE International Conference on Robotics and Automation.

[14]  T. Stoffregen.  Affordances as Properties of the Animal-Environment System , 2003, How Shall Affordances be Refined? Four Perspectives.

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  M. Huber,et al.  Learning Behaviorally Grounded State Representations for Reinforcement Learning Agents , 2006 .

[17]  Maya Çakmak.  ROBOT PLANNING BASED ON LEARNED AFFORDANCES A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF THE MIDDLE EAST TECHNICAL UNIVERSITY BY MAYA ÇAKMAK IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE , 2007 .

[18]  W. Warren,et al.  Visual guidance of walking through apertures: body-scaled information for affordances. , 1987, Journal of experimental psychology. Human perception and performance.

[19]  Giulio Sandini,et al.  Learning about objects through action - initial steps towards artificial cognition , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[20]  Karl F. MacDorman,et al.  Responding to affordances: learning and projecting a sensorimotor mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[21]  Maya Cakmak,et al.  The learning and use of traversability affordance using range images on a mobile robot , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[22]  Robert St. Amant,et al.  Planning and user interface affordances , 1998, IUI '99.

[23]  Mark Steedman,et al.  Plans, Affordances, And Combinatory Grammar , 2002 .

[24]  M. Turvey.  Affordances and Prospective Control: An Outline of the Ontology , 1992 .