Interplay between neural computational energy and multimodal processing in robot-robot interaction

Name: Interplay between neural computational energy and multimodal processing in robot-robot interaction
Author: Kırtay, M., Hafner, V. V., Asada, M., Öztop, Erhan

İsim	Interplay between neural computational energy and multimodal processing in robot-robot interaction
Yazar	Kırtay, M., Hafner, V. V., Asada, M., Öztop, Erhan
Basım Tarihi:	2023
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166547075-9
Kayıt Numarası	ad2cf3d4-0764-49a7-bf84-92a99ffd17bd
Lokasyon	Computer Science
Tarih	2023
Notlar	Deutsche Forschungsgemeinschaft ; New Energy and Industrial Technology Development Organization ; Japan Society for the Promotion of Science ; Deutsche Forschungsgemeinschaft
Örnek Metin	Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not been addressed in robotics, especially in Robot-Robot Interaction (RRI). This study addresses the role of computational cost in multimodal processing by considering robot-robot interaction in a sequential multimodal memory recall task. In this setting, the learner (Nao) robot receives auditory-only, visual-only, or audio-visual information from an instructor (Pepper) robot and the environment regarding previously learned memory items. The goal of the learner robot is to perform the interactive task with as low as possible neural computational cost. The learner robot has two cognitive modules: a multimodal auto-associative network that stands for the perceptual-cognitive processing of the robot and an internal reward mechanism that monitors the changes in neural energy incurred for two consecutive steps by the processing of the attended stimuli. The reward computed is used to build an action policy for minimizing the neural energy consumption over the sequential memory recall task. The experimental results show that having access to both auditory and visual information is beneficial not only for better memory recall but also for minimizing the cost of neural computation.
DOI	10.1109/ICDL55364.2023.10364527

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Interplay between neural computational energy and multimodal processing in robot-robot interaction

Yazar Kırtay, M., Hafner, V. V., Asada, M., Öztop, Erhan

Basım Tarihi 2023

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166547075-9

Kayıt Numarası ad2cf3d4-0764-49a7-bf84-92a99ffd17bd

Lokasyon Computer Science

Tarih 2023

Notlar Deutsche Forschungsgemeinschaft ; New Energy and Industrial Technology Development Organization ; Japan Society for the Promotion of Science ; Deutsche Forschungsgemeinschaft

Örnek Metin Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not been addressed in robotics, especially in Robot-Robot Interaction (RRI). This study addresses the role of computational cost in multimodal processing by considering robot-robot interaction in a sequential multimodal memory recall task. In this setting, the learner (Nao) robot receives auditory-only, visual-only, or audio-visual information from an instructor (Pepper) robot and the environment regarding previously learned memory items. The goal of the learner robot is to perform the interactive task with as low as possible neural computational cost. The learner robot has two cognitive modules: a multimodal auto-associative network that stands for the perceptual-cognitive processing of the robot and an internal reward mechanism that monitors the changes in neural energy incurred for two consecutive steps by the processing of the attended stimuli. The reward computed is used to build an action policy for minimizing the neural energy consumption over the sequential memory recall task. The experimental results show that having access to both auditory and visual information is beneficial not only for better memory recall but also for minimizing the cost of neural computation.

DOI 10.1109/ICDL55364.2023.10364527