A multi-layer artificial intelligence and sensing based affective conversational embodied agent

DiPaola, Steve; Yalçın, Özge Nilay

doi:10.1109/aciiw.2019.8925291

Cited by 7 publications

(10 citation statements)

References 3 publications

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“…Our perception module did not assign a positive or negative emotional state and it did not involve training an additional model from the observed action units; rather, it detected and provided as output the appearance of the abovementioned action units. The detected action units signified the person was exhibiting one of the selected facial expressions, which is an approach often used in studies where the perceptual component in the architecture is focused only on facial expressions, and not on more detailed emotional state (Yalcin and DiPaola, 2018 ; DiPaola and Yalçin, 2019 ).…”

Section: Our Frameworkmentioning

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: Our Frameworkmentioning

confidence: 99%

A Socially Adaptable Framework for Human-Robot Interaction

et al. 2020

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“…The virtual moderator has a positive influence on users’ behaviours and cognition, and emotions through the Proteus and social identification effects enabled by VR technology [ 7 , 53 , 54 ]. From an AI perspective, deep learning models enable empathic conversations by introducing features such as emotion recognition, eye gaze, and voice stress [ 73 ], which are crucial to building intimacy between digital humans and users [ 39 ]. Therefore, AI provides a step change towards producing enhanced methods of communication that are as realistic as possible within a virtual environment.…”

Section: Discussionmentioning

confidence: 99%

“…Gesture-based communication has been highly effective in inducing effects of co-presence [ 77 ]. When combined with other communication enhancers including emotion recognition, eye contact [ 73 ], a positive immersive experience, is achieved, which leads to creative discussions. Intelligent HCI and deep learning approaches play a vital role in enabling multiple modes of communication [ 75 ].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, intimacy can be developed from this two-way communication that creates an emotional connection with humans [ 39 ]. DiPaola and Yalçin [ 73 ] addressed the complexity behind building natural conversations between virtual humans through an AI-based deep learning approach which involved human related inputs such as facial emotion recognition, eye gaze, and voice stress to create an AI avatar system that allows empathetic conversations between virtual agents and users [ 73 ].…”

Section: Integrating Artificial Intelligence (Ai) and Creativitymentioning

confidence: 99%

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An Extended AI-Experience: Industry 5.0 in Creative Product Innovation

Grech

Mehnen

Wodehouse

2023

Sensors

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Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the engineering field. A bibliographic analysis is performed to review relevant fields and their relationships. This is followed by a review of current challenges in group ideation and state-of-the-art technologies with the aim of addressing them in this study. This knowledge is applied to the transformation of current ideation scenarios into a virtual environment using AI. The aim is to augment designers’ creative experiences, a core value of Industry 5.0 that focuses on human-centricity, social and ecological benefits. For the first time, this research reclaims brainstorming as a challenging and inspiring activity where participants are fully engaged through a combination of AI and VR technologies. This activity is enhanced through three key areas: facilitation, stimulation, and immersion. These areas are integrated through intelligent team moderation, enhanced communication techniques, and access to multi-sensory stimuli during the collaborative creative process, therefore providing a platform for future research into Industry 5.0 and smart product development.

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“…Multiagent and synergetic constellation awareness overcome these limitations. Moreover, embodied intelligence [286] and deep, general, and evolutionary learning can be applied to multiagent systems and constellations for realistic multimodal interaction. They contribute to the intelligent evolution of situational awareness systems…”

Section: Discussionmentioning

confidence: 99%

Research Advancements in Key Technologies for Space-Based Situational Awareness

Wang

et al. 2022

Space Sci Technol

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The space environment has become highly congested due to the increasing space debris, seriously threatening the safety of orbiting spacecraft. Space-based situational awareness, as a comprehensive capability of threat knowledge, analysis, and decision-making, is of significant importance to ensure space security and maintain normal order. Various space situational awareness systems have been designed and launched. Data acquisition, target recognition, and monitoring constituting key technologies make major contributions, and various advanced algorithms are explored as technical supports. However, comprehensive reviews of these technologies and specific algorithms rarely emerge. It disadvantages the future development of space situational awareness. Therefore, this paper further reviews and analyzes research advancements in key technologies for space situational awareness, emphasizing target recognition and monitoring. Many mature and emerging methods are presented for these technologies while discussing application advantages and limitations. Specially, the research prospects of multiagent and synergetic constellation technologies are expected for future situational awareness. This paper indicates the future directions of the key technologies, aiming to provide references for space-based situational awareness to realize space sustainability.

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A multi-layer artificial intelligence and sensing based affective conversational embodied agent

Cited by 7 publications

References 3 publications

A Socially Adaptable Framework for Human-Robot Interaction

A Socially Adaptable Framework for Human-Robot Interaction

An Extended AI-Experience: Industry 5.0 in Creative Product Innovation

Research Advancements in Key Technologies for Space-Based Situational Awareness

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