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TitleCombining intention and emotional state inference in a dynamic neural field architecture for human-robot joint action
Author(s)Silva, Rui
Louro, Luís
Malheiro, Tiago
Erlhagen, Wolfram
Bicho, Estela
KeywordsHuman-robot interactions
Dyanmic field architecture
Goal inference
Emotional state
Human-robot joint action
emotional state inference
error detection
decision making
dynamic neural fields
Issue date10-Oct-2016
PublisherSAGE Publications
JournalAdaptive Behavior
Abstract(s)We report on our approach towards creating socially intelligent robots, which is heavily inspired by recent experimental findings about the neurocognitive mechanisms underlying action and emotion understanding in humans. Our approach uses neuro-dynamics as a theoretical language to model cognition, emotional states, decision making and action. The control architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode relevant information in the form of self-sustained activation patterns, which are triggered by input from connected populations and evolve continuously in time. The architecture implements a dynamic and flexible context-dependent mapping from observed hand and facial actions of the human onto adequate complementary behaviors of the robot that take into account the inferred goal and inferred emotional state of the co-actor. The dynamic control architecture was validated in multiple scenarios in which an anthropomorphic robot and a human operator assemble a toy object from its components. The scenarios focus on the robot’s capacity to understand the human’s actions, and emotional states, detect errors and adapt its behavior accordingly by adjusting its decisions and movements during the execution of the task.
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals
DEI - Artigos em revistas internacionais

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