Tag Archives: Affordances

Dealing with affordances in robotics through RL

X. Yang, Z. Ji, J. Wu and Y. -K. Lai, Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective, EEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 3, pp. 1139-1149, Sept. 2023 DOI: 10.1109/TCDS.2023.3277288.

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This article provides a short review of the recent developments of deep robotic affordance learning (DRAL), which aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective and draw connections between RL and affordances. The technical details of each category are discussed and their limitations are identified. We further summarize them and identify future challenges from the aspects of observations, actions, affordance representation, data-collection, and real-world deployment. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.

Survey on the concept of affordance and its use in robotics (the rest of this issue of the journal also deals with affordances in robotics)

L. Jamone et al, Affordances in Psychology, Neuroscience, and Robotics: A Survey,, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 1, pp. 4-25, March 2018, DOI: 10.1109/TCDS.2016.2594134.

The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics.