Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions.
Publication Date
2022Journal Title
Front Robot AI
ISSN
2296-9144
Publisher
Frontiers Media SA
Volume
9
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Churamani, N., Barros, P., Gunes, H., & Wermter, S. (2022). Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions.. Front Robot AI, 9 https://doi.org/10.3389/frobt.2022.717193
Abstract
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.
Keywords
Robotics and AI, human-robot interaction, multi-modal affect perception, core affect, reinforcement learning, neural networks
Sponsorship
Engineering and Physical Sciences Research Council (EP/R030782/1)
EPSRC (2107412)
Identifiers
717193
External DOI: https://doi.org/10.3389/frobt.2022.717193
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334724
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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