Building Affordance Relations for Robotic Agents - A Review

TL;DR

This review paper explores how the concept of affordances can be integrated into robotic systems to enhance autonomy, discussing strategies, design choices, and future research directions for AI agents.

Contribution

It synthesizes existing approaches to modeling affordances in robotics, providing a unified framework and guidance for future development.

Findings

Common strategies for representing affordances in robotics

Design choices impacting generalization to new scenarios

Potential research directions to improve AI agent capabilities

Abstract

Affordances describe the possibilities for an agent to perform actions with an object. While the significance of the affordance concept has been previously studied from varied perspectives, such as psychology and cognitive science, these approaches are not always sufficient to enable direct transfer, in the sense of implementations, to artificial intelligence (AI)-based systems and robotics. However, many efforts have been made to pragmatically employ the concept of affordances, as it represents great potential for AI agents to effectively bridge perception to action. In this survey, we review and find common ground amongst different strategies that use the concept of affordances within robotic tasks, and build on these methods to provide guidance for including affordances as a mechanism to improve autonomy. To this end, we outline common design choices for building representations of affordance relations, and their implications on the generalisation capabilities of an agent when facing previously unseen scenarios. Finally, we identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.