Associating word descriptions to learned manipulation task models
2008 (English)In: IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), Nice, France, 2008Conference 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. This knowledge is acquired by the robot in an unsupervised way by self-interaction with the environment. When a human user is involved in the process and describes a particular task, the robot can form associations between the (co-occurrence of) speech utterances and the involved objects, actions and effects. We extend the affordance model to incorporate a simple description of speech as a set of words. We show that, across many experiences, 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. Word-to-meaning associations are then used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.
Place, publisher, year, edition, pages
Nice, France, 2008.
Computer Science Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-52070OAI: oai:DiVA.org:kth-52070DiVA: diva2:465364
IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), Nice, France, 2008
tmh_import_11_12_14. QC 201201042011-12-142011-12-142012-01-04Bibliographically approved