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Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions

Collaborative interactions require social robots to share the users’ perspective on the interactions and adapt to the dynamics of their affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between...

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Autores principales: Churamani, Nikhil, Barros, Pablo, Gunes, Hatice, Wermter, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898942/
https://www.ncbi.nlm.nih.gov/pubmed/35265672
http://dx.doi.org/10.3389/frobt.2022.717193
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author Churamani, Nikhil
Barros, Pablo
Gunes, Hatice
Wermter, Stefan
author_facet Churamani, Nikhil
Barros, Pablo
Gunes, Hatice
Wermter, Stefan
author_sort Churamani, Nikhil
collection PubMed
description Collaborative interactions require social robots to share the users’ perspective on the interactions and adapt to the dynamics of their affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed human expressions and static robot actions. In this paper, we propose a novel framework for affect-driven behaviour generation in social robots. The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech of the users, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed behavioural traits like patience and emotional actuation that modulate the robot’s affective appraisal, and (iii) a Reinforcement Learning model that uses the robot’s appraisal to learn interaction behaviour. We investigate the effect of modelling different affective core dispositions on the affective appraisal and use this affective appraisal as the motivation to generate robot behaviours. For evaluation, we conduct a user study (n = 31) where the NICO robot acts as a proposer in the Ultimatum Game. The effect of the robot’s affective core on its negotiation strategy is witnessed by participants, who rank a patient robot with high emotional actuation higher on persistence, while an impatient robot with low emotional actuation is rated higher on its generosity and altruistic behaviour.
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spelling pubmed-88989422022-03-08 Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions Churamani, Nikhil Barros, Pablo Gunes, Hatice Wermter, Stefan Front Robot AI Robotics and AI Collaborative interactions require social robots to share the users’ perspective on the interactions and adapt to the dynamics of their affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed human expressions and static robot actions. In this paper, we propose a novel framework for affect-driven behaviour generation in social robots. The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech of the users, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed behavioural traits like patience and emotional actuation that modulate the robot’s affective appraisal, and (iii) a Reinforcement Learning model that uses the robot’s appraisal to learn interaction behaviour. We investigate the effect of modelling different affective core dispositions on the affective appraisal and use this affective appraisal as the motivation to generate robot behaviours. For evaluation, we conduct a user study (n = 31) where the NICO robot acts as a proposer in the Ultimatum Game. The effect of the robot’s affective core on its negotiation strategy is witnessed by participants, who rank a patient robot with high emotional actuation higher on persistence, while an impatient robot with low emotional actuation is rated higher on its generosity and altruistic behaviour. Frontiers Media S.A. 2022-02-21 /pmc/articles/PMC8898942/ /pubmed/35265672 http://dx.doi.org/10.3389/frobt.2022.717193 Text en Copyright © 2022 Churamani, Barros, Gunes and Wermter. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Churamani, Nikhil
Barros, Pablo
Gunes, Hatice
Wermter, Stefan
Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title_full Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title_fullStr Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title_full_unstemmed Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title_short Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions
title_sort affect-driven learning of robot behaviour for collaborative human-robot interactions
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898942/
https://www.ncbi.nlm.nih.gov/pubmed/35265672
http://dx.doi.org/10.3389/frobt.2022.717193
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