Designing an Affective Cognitive Architecture for Human-Humanoid Interaction

Tanevska, Ana; Sandini, Giulio; Sciutti, Alessandra

doi:10.1145/3173386.3177035

Cited by 20 publications

(13 citation statements)

References 3 publications

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“…In our past works, we have explored adaptation in child-robot interaction (CRI) in the context of switching between different game-based behaviors (Tanevska, 2016 ). The architecture was affect-based (Tanevska et al, 2018b ), and the robot could express three basic emotions (a “happy,” a “sad,” and a “neutral” state) in a simple way. These emotions were affected by the level of engagement the child felt toward the current robot's behavior.…”

Section: Introductionmentioning

confidence: 99%

A Socially Adaptable Framework for Human-Robot Interaction

et al. 2020

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In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could be guided by an internal motivation system. We wish to investigate how an adaptive robotic framework of this kind would function and personalize to different users. We also wish to explore whether the adaptability and personalization would bring any additional richness to the human-robot interaction (HRI), or whether it would instead bring uncertainty and unpredictability that would not be accepted by the robot's human peers. To this end, we designed a socially adaptive framework for the humanoid robot iCub. As a result, the robot perceives and reuses the affective and interactive signals from the person as input for the adaptation based on internal social motivation. We strive to investigate the value of the generated adaptation in our framework in the context of HRI. In particular, we compare how users will experience interaction with an adaptive versus a non-adaptive social robot. To address these questions, we propose a comparative interaction study with iCub whereby users act as the robot's caretaker, and iCub's social adaptation is guided by an internal comfort level that varies with the stimuli that iCub receives from its caretaker. We investigate and compare how iCub's internal dynamics would be perceived by people, both in a condition when iCub does not personalize its behavior to the person, and in a condition where it is instead adaptive. Finally, we establish the potential benefits that an adaptive framework could bring to the context of repeated interactions with a humanoid robot.

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Section: Introductionmentioning

confidence: 99%

A Socially Adaptable Framework for Human-Robot Interaction

et al. 2020

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“…Some papers assess human-robot interactions with objective metrics based on the measure of human states. In [11], they proposed a framework allowing the robot to perceive and evaluate in real-time the affective state and the engagement state of the people it is interacting with. In [12], a bio-instrumentation system is proposed in order to evaluate human stress in real-time by measuring physiological parameters such as respiration, heart rate, perspiration, pulse wave and arm motion.…”

Section: Related Workmentioning

confidence: 99%

“…Only a few papers propose a scheme which "closes the loop" between evaluation and the adaptation of the robot behaviour. In [11], the evaluation of the affective and engagement states of the human is used by the robot decision making processes. In [12], the robot executes a motion specifically designed to decrease human stress when assessed too high.…”

Section: Related Workmentioning

confidence: 99%

Toward a Robot Computing an Online Estimation of the Quality of its Interaction with its Human Partner

Mayima

Clodic

Alami

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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When we perform a collaborative task with another human, we are able to tell, to a certain extent, how things are going and more precisely if things are going well or not. This knowledge allows us to adapt our behavior. Therefore, we think it is desirable to provide robots with means to measure in real-time the Quality of the Interaction with their human partners. To make this possible, we propose a model and a set of metrics targeting the evaluation of the QoI in collaborative tasks through the measure of the human engagement and the online task effectiveness. These model and metrics have been implemented and tested within the high-level controller of an entertainment robot deployed in a mall. The first results show significant differences in the computed QoI when in interaction with a fully compliant human, a confused human and a noncooperative one.

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“…iCub is a humanoid robot [ 99 ], which has a cognitive-affective architecture. The architecture is a kind of loop, which is composed of (1) perceiving the emotional state, (2) predicting which action would be the most beneficial for the robot and human, and then performing the most beneficial action, and (3) evaluating from the perceptual input if the person’s reaction was predicted, modifying the belief values if wrong and reinforcing them if right.…”

Section: Resultsmentioning

confidence: 99%

“…However, the architecture is oriented on conversational agents and not robotic systems. Additionally, in 2017, Tanevska et al [ 99 ] proposed an affective cognitive architecture for the iCub robot. iCub can perceive and evaluate emotional states.…”

Section: Resultsmentioning

confidence: 99%

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review

Cano

González

Gil

et al. 2021

Sensors

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Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitive-affective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.

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Designing an Affective Cognitive Architecture for Human-Humanoid Interaction

Cited by 20 publications

References 3 publications

A Socially Adaptable Framework for Human-Robot Interaction

A Socially Adaptable Framework for Human-Robot Interaction

Toward a Robot Computing an Online Estimation of the Quality of its Interaction with its Human Partner

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review

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