Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/10962

TitleA dynamic neural field architecture for a pro-active assistant robot
Author(s)Pinheiro, Manuel
Bicho, E.
Erlhagen, Wolfram
KeywordsAssistant robot
Pro-active behavior
Action understanding
Goal inference
Dynamic neural fields
Dynamical systems
Anthropomorphic robot
Dynamic control architecture
Motor imparments
Anticipatory behavior
Coordination of actions and decisions
Joint action
Human-robot interaction
Issue dateSep-2010
PublisherIEEE
JournalProceedings of the Ieee Ras-Embs International Conference on Biomedical Robotics and Biomechatronics
CitationIEEE RAS & EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB 2010), 3, Tokyo, Japan, 2010 – “Proceedings of the 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2010)”. [S.l.] : IEEE, cop. 2010. ISBN 978-1-4244-7707-4. p. 777-784.
Abstract(s)We present a control architecture for non-verbal HRI that allows an assistant robot to have a pro-active and anticipatory behavior. The architecture implements the coordination of actions and goals among the human, that needs help, and the robot as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of the human motor behavior. The robot control architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. Different subpopulations encode task relevant information about action means, action goals and context in form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic control architecture is validated in an assistive task in which an anthropomorphic robot acts as a personal assistant of a person with motor impairments. We show that the context dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing situations. This includes adaptation to different users and mutual compensation of physical limitations.
TypeConference paper
URIhttp://hdl.handle.net/1822/10962
ISBN978-1-4244-7707-4
DOI10.1109/BIOROB.2010.5627812
ISSN2155-1782
Publisher version(c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review
DEI - Artigos em atas de congressos internacionais

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