Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction

Name: Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
Author: Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V:

İsim	Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
Yazar	Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V:
Basım Tarihi:	2021-08-08
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-6654-0492-1
Kayıt Numarası	9cb86f23-766b-48ca-984d-536552e31402
Lokasyon	Computer Science
Tarih	2021-08-08
Notlar	Deutsche Forschungsgemeinschaft ; Osaka University
Örnek Metin	Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in human-robot and robot-robot interaction studies. In this paper, we provide a unified approach to address these issues in robot-robot interaction settings. To be concrete, we present a trust-based affective computational account of scaffolding while performing a sequential visual recalling task. In that, we endow the Pepper humanoid robot with cognitive modules of auto-associative memory and internal reward generation to implement the trust model. The former module is an instance of a cognitive function with an associated neural cost determining the cognitive load of performing visual memory recall. The latter module uses this cost to generate an internal reward signal to facilitate neural cost-based reinforcement learning (RL) in an interactive scenario involving online instructors with different guiding strategies: reliable, less-reliable, and random. These cognitive modules allow the Pepper robot to assess the instructors based on the average cumulative reward it can collect and choose the instructor that helps reduce its cognitive load most as the trustworthy one. After determining the trustworthy instructor, the Pepper robot is recruited to be a caregiver robot to guide a perceptually limited infant robot (i.e., the Nao robot) that performs the same task. In this setting, we equip the Pepper robot with a simple theory of mind module that learns the state-action-reward associations by observing the infant robot's behavior and guides the learning of the infant robot, similar to when it went through the online agent-robot interactions. The experiment results on this robot-robot interaction scenario indicate that the Pepper robot as a caregiver leverages the decision-making policies - obtained by interacting with the trustworthy instructor- to guide the infant robot to perform the same task efficiently. Overall, this study suggests how robotic-trust can be grounded in human-robot or robot-robot interactions based on cognitive load, and be used as a mechanism to choose the right scaffolding agent for effective knowledge transfer.
DOI	10.1109/RO-MAN50785.2021.9515494

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Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction

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

Basım Tarihi 2021-08-08

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-6654-0492-1

Kayıt Numarası 9cb86f23-766b-48ca-984d-536552e31402

Lokasyon Computer Science

Tarih 2021-08-08

Notlar Deutsche Forschungsgemeinschaft ; Osaka University

Örnek Metin Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in human-robot and robot-robot interaction studies. In this paper, we provide a unified approach to address these issues in robot-robot interaction settings. To be concrete, we present a trust-based affective computational account of scaffolding while performing a sequential visual recalling task. In that, we endow the Pepper humanoid robot with cognitive modules of auto-associative memory and internal reward generation to implement the trust model. The former module is an instance of a cognitive function with an associated neural cost determining the cognitive load of performing visual memory recall. The latter module uses this cost to generate an internal reward signal to facilitate neural cost-based reinforcement learning (RL) in an interactive scenario involving online instructors with different guiding strategies: reliable, less-reliable, and random. These cognitive modules allow the Pepper robot to assess the instructors based on the average cumulative reward it can collect and choose the instructor that helps reduce its cognitive load most as the trustworthy one. After determining the trustworthy instructor, the Pepper robot is recruited to be a caregiver robot to guide a perceptually limited infant robot (i.e., the Nao robot) that performs the same task. In this setting, we equip the Pepper robot with a simple theory of mind module that learns the state-action-reward associations by observing the infant robot's behavior and guides the learning of the infant robot, similar to when it went through the online agent-robot interactions. The experiment results on this robot-robot interaction scenario indicate that the Pepper robot as a caregiver leverages the decision-making policies - obtained by interacting with the trustworthy instructor- to guide the infant robot to perform the same task efficiently. Overall, this study suggests how robotic-trust can be grounded in human-robot or robot-robot interactions based on cognitive load, and be used as a mechanism to choose the right scaffolding agent for effective knowledge transfer.

DOI 10.1109/RO-MAN50785.2021.9515494