Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Name: Trustworthiness assessment in multimodal human-robot interaction based on cognitive load
Author: Kırtay, M., Öztop, Erhan, Kuhlen, A. K., asa, M., Hafner, V. V.

İsim	Trustworthiness assessment in multimodal human-robot interaction based on cognitive load
Yazar	Kırtay, M., Öztop, Erhan, Kuhlen, A. K., asa, M., Hafner, V. V.
Basım Tarihi:	2022
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-172818859-1
Kayıt Numarası	536447f1-1f34-4ea2-9377-3053430db36d
Lokasyon	Computer Science
Tarih	2022
Notlar	Deutsche Forschungsgemeinschaft
Örnek Metin	In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.
DOI	10.1109/RO-MAN53752.2022.9900730

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Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Yazar Kırtay, M., Öztop, Erhan, Kuhlen, A. K., asa, M., Hafner, V. V.

Basım Tarihi 2022

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-172818859-1

Kayıt Numarası 536447f1-1f34-4ea2-9377-3053430db36d

Lokasyon Computer Science

Tarih 2022

Notlar Deutsche Forschungsgemeinschaft

Örnek Metin In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.

DOI 10.1109/RO-MAN53752.2022.9900730