To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control

By Erol Sahin, Maya Cakmak, M. Dogar, Emre Ugur, G. Üçoluk. Adaptive Behavior, 2007

384 citations recorded by the source corpus.

The concept of affordances was introduced by J. J. Gibson to eXplain how inherent “values” and “meanings” of things in the environment can be directly perceived and how this information can be linked to the action possibilities offered to the organism by the environment. Although introduced in psychology, the concept influenced studies in other fields ranging from human—computer interaction to autonomous robotics. In this article, we first introduce the concept of affordances as conceived by J. J. Gibson and review the use of the term in different fields, with particular emphasis on its use in autonomous robotics. Then, we summarize four of the major formalization proposals for the affordance term. We point out that there are three, not one, perspectives from which to view affordances and that much of the confusion regarding discussions on the concept has arisen from this. We propose a new formalism for affordances and discuss its implications for autonomous robot control. We report preliminary results obtained with robots and link them with these implications.

Subfields: Cognitive Neuroscience, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/82b0b32a6a500c2a15830f169b74a8a6ccf63463

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Citations in the corpus

KETO: Learning Keypoint Representations for Tool Manipulation (2019, methodology): “To enable robots to understand and manipulate tools, several pioneer works focus on predicting affordance and functional regions on tool objects [30], [48], [41], [58], [27], [14].”
Taskability Graph: Towards analyzing effort based agent-agent affordances (2012, neutral): “In robotics affordance has been viewed from different perspectives: agent, observer and environment, [3].”
Abstraction Levels for Robotic Imitation: Overview and Computational Approaches (2010, background): “A thorough review on these topics can be found in [122], with special emphasis placed in affordance-based control.”
Neural mechanisms for interacting with a world full of action choices. (2010, background): “Sakagami & Pan (2007) suggest that this information is further integrated with sensory signals from the ventral visual stream to provide an estimate of the behavioral relevance of potential actions in the ventrolateral prefrontal cortex (VLPFC), which then projects to the DLFPC and premotor regions…”
Autonomous acquisition of pushing actions to support object grasping with a humanoid robot (2009, background): “Pushing has often been used as an example when learning affordances [13], but this type of research normally focuses on higher-level knowledge such as "this ball affords pushing".”
Learning Generalizable Control Programs (2011, background): “and the MultiSensory Autonomous Cognitive Systems group have provided formalisms that capture the statistical relationships between taking actions on objects and observing the effects [20], [29].”