Affordance based word-to-meaning association
2009 (English)In: ICRA: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VDE Verlag GmbH, 2009, 4138-4143 p.Conference paper (Refereed)
This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. We extend the affordance model to incorporate words. Using verbal descriptions of a task, the model uses temporal co-occurrence to create links between speech utterances and the involved objects, actions and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.
Place, publisher, year, edition, pages
VDE Verlag GmbH, 2009. 4138-4143 p.
, Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Auditory system, Bayesian methods, Context, Human robot interaction, Inference algorithms, Iterative methods, Robot sensing systems, Robotics and automation, Speech recognition, Statistical learning
Computer Science Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-52071DOI: 10.1109/ROBOT.2009.5152306ISI: 000276080400131ScopusID: 2-s2.0-70350383489ISBN: 978-1-4244-2788-8OAI: oai:DiVA.org:kth-52071DiVA: diva2:465365
2009 IEEE International Conference on Robotics and Automation, ICRA '09; Kobe; 12 May 2009 through 17 May 2009
tmh_import_11_12_14 QC 201112202011-12-142011-12-142011-12-20Bibliographically approved