A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks

One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widel...

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Autores principales:	Omar Eldardeer, Jonas Gonzalez-Billandon, Lukas Grasse, Matthew Tata, Francesco Rea
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	joint attention multisensory integration memory decision-making computational neuroscience human robot interaction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/349fdab84e594e8788f69243b8313ddc
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id	oai:doaj.org-article:349fdab84e594e8788f69243b8313ddc
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spelling	oai:doaj.org-article:349fdab84e594e8788f69243b8313ddc2021-11-30T14:01:16ZA Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks1662-521810.3389/fnbot.2021.648595https://doaj.org/article/349fdab84e594e8788f69243b8313ddc2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbot.2021.648595/fullhttps://doaj.org/toc/1662-5218One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.Omar EldardeerOmar EldardeerJonas Gonzalez-BillandonJonas Gonzalez-BillandonLukas GrasseMatthew TataFrancesco ReaFrontiers Media S.A.articlejoint attentionmultisensory integrationmemorydecision-makingcomputational neurosciencehuman robot interactionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neurorobotics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	joint attention multisensory integration memory decision-making computational neuroscience human robot interaction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	joint attention multisensory integration memory decision-making computational neuroscience human robot interaction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Omar Eldardeer Omar Eldardeer Jonas Gonzalez-Billandon Jonas Gonzalez-Billandon Lukas Grasse Matthew Tata Francesco Rea A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
description	One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.
format	article
author	Omar Eldardeer Omar Eldardeer Jonas Gonzalez-Billandon Jonas Gonzalez-Billandon Lukas Grasse Matthew Tata Francesco Rea
author_facet	Omar Eldardeer Omar Eldardeer Jonas Gonzalez-Billandon Jonas Gonzalez-Billandon Lukas Grasse Matthew Tata Francesco Rea
author_sort	Omar Eldardeer
title	A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
title_short	A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
title_full	A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
title_fullStr	A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
title_full_unstemmed	A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks
title_sort	biological inspired cognitive framework for memory-based multi-sensory joint attention in human-robot interactive tasks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/349fdab84e594e8788f69243b8313ddc
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A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks

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