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Recent advances of deep robotic affordance learning: a reinforcement learning perspective

Yang, Xintong ORCID:, Ji, Ze ORCID:, Wu, Jing ORCID: and Lai, Yu-Kun ORCID: 2023. Recent advances of deep robotic affordance learning: a reinforcement learning perspective. IEEE Transactions on Cognitive and Developmental Systems 15 (3) , pp. 1139-1149. 10.1109/TCDS.2023.3277288

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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 paper 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 summarise 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISSN: 2379-8920
Date of First Compliant Deposit: 15 May 2023
Date of Acceptance: 14 May 2023
Last Modified: 02 Nov 2023 15:16

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