Affordances in Robotic Tasks -- A Survey

Abstract

Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects. Historically, the concept derives from the literature in psychology and cognitive science, where affordances are discussed in a way that makes it hard for the definition to be directly transferred to computational specifications useful for robots. This review article is focused specifically on robotics, so we discuss the related literature from this perspective. In this survey, we classify the literature and try to find common ground amongst different approaches with a view to application in robotics. We propose a categorisation based on the level of prior knowledge that is assumed to build the relationship among different affordance components that matter for a particular robotic task. We also identify areas for future improvement and discuss possible directions that are likely to be fruitful in terms of impact on robotics practice.

arXiv commentManuscript currently under review

References in corpus (3)

• RoboBrain: Large-Scale Knowledge Engine for Robots
• AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
• Physical Primitive Decomposition

Cited by in corpus (4)

• Semantics for Robotic Mapping, Perception and Interaction: A Survey
• Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
• Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?
• Bayesian deep learning of affordances from RGB images