Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/17339

TitleA dynamic field approach to goal inference, error detection and anticipatory action selection in human-robot collaboration
Author(s)Bicho, E.
Erlhagen, Wolfram
Louro, Luís
Silva, Eliana Oliveira Costa
Silva, Rui Manuel Gomes
Hipólito, Nzoji
KeywordsHuman-robot interaction
Human-robot collaboration
Goal inference
Error detection
Anticipation
Action selection
Dynamic Fields
Issue dateNov-2011
PublisherJohn Benjamins Publishing Company
Abstract(s)In this chapter we present results of our ongoing research on efficient and fluent human-robot collaboration that is heavily inspired by recent experimental findings about the neurocognitive mechanisms supporting joint action in humans. The robot control architecture implements the joint coordination of actions and goals as a dynamic process that integrates contextual cues, shared task knowledge and the predicted outcome of the user's motor behavior. The architecture is formalized as a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. We validate the approach in a task in which a robot and a human user jointly construct a toy 'vehicle'. We show that the context-dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing joint action situations. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection.
TypeBook part
URIhttp://hdl.handle.net/1822/17339
ISBN978 90 272 8339 9
Publisher versionhttp://benjamins.com/#catalog/books/ais.2.10bic/details
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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