Robot learning and use of affordances in goal-directed tasks

Chang Wang, Koen V. Hindriks, Robert Babuska

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

An affordance is a relation between an object, an action, and the effect of that action in a given environmental context. One key benefit of the concept of affordance is that it provides information about the consequence of an action which can be stored and reused in a range of tasks that a robot needs to learn and perform. In this paper, we address the challenge of the on-line learning and use of affordances simultaneously while performing goal-directed tasks. This requires efficient online performance to ensure the robot is able to achieve its goal fast. By providing conceptual knowledge of action possibilities and desired effects, we show that a humanoid robot NAO can learn and use affordances in two different task settings. We demonstrate the effectiveness of this approach by integrating affordances into an Extended Classifier System for learning general rules in a reinforcement learning framework. Our experimental results show significant speedups in learning how a robot solves a given task.

Original languageEnglish
Title of host publicationIROS 2013
Subtitle of host publicationNew Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages2288-2294
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: 3 Nov 20138 Nov 2013

Conference

Conference2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
CountryJapan
CityTokyo
Period3/11/138/11/13

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