Learning Affordances for Assistive Robots

Mohan Sridharan, Ben Meadows

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This paper describes an architecture that enables a robot to represent,
reason about, and learn affordances. Specifically, Answer Set Prolog is used
to represent and reason with incomplete domain knowledge that includes affordances modeled as relations between attributes of the robot and the object(s) in the context of specific actions. The learning of affordance relations from observations obtained through reactive execution or active exploration is formulated as a reinforcement learning problem. A sampling-based approach and decision-tree regression with the underlying relational representation are used to obtain generic affordance relations that are added to the Answer Set Prolog program for subsequent reasoning. The capabilities of this architecture are illustrated and evaluated in the context of a simulated robot assisting humans in an indoor domain.
Original languageEnglish
Title of host publicationSocial Robotics
Subtitle of host publication9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings
EditorsA. Kheddar, E. Yoshida, S.S. Ge, K. Suzuki, J.-J. Cabibihan, F. Eyssel, H. He
PublisherSpringer
Chapter1
Pages1-11
ISBN (Electronic)978-3-319-70022-9
ISBN (Print)978-3-319-70021-2
DOIs
Publication statusPublished - 24 Oct 2017
Event9th International Conference on Social Robotics 2017 (ICSR) - Tsukuba, Japan
Duration: 22 Nov 201724 Nov 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10652
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Social Robotics 2017 (ICSR)
Country/TerritoryJapan
CityTsukuba
Period22/11/1724/11/17

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