EMRES: A New EMotional RESpondent Robot

Sönmez, Elena Battini; Han, Hasan; Karadeniz, Oguzcan; Dalyan, Tuğba; Sarioğlu, Baykal

doi:10.1109/tcds.2021.3120562

Cited by 3 publications

(7 citation statements)

References 38 publications

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“…Handcrafted non-verbal gestures are selected based on the robot’s emotion, and repeated based on the duration of the response. Sönmez et al [ 42 ] presented an artificial empathetic two-layer system, where the first layer maintains the robot’s internal state, and the second controls the robot’s flow of emotions. This flow is affected by the robot’s current emotion, its internal state and mood (updated based on the last five emotional states), and the user’s perceived emotion.…”

Section: Related Workmentioning

confidence: 99%

“…While speech, motions, facial expressions (including gaze), and LEDs are the main modalities used in the works reviewed, there are also other channels that have been explored. For example, Sönmez et al [ 42 ] included a touch screen that the robot would use to display emojis that represented its internal state. Finally, Klausen et al [ 44 ] proposed using different breathing patterns in a soft robot to convey emotions, by inflating and deflating the body of the robot.…”

Section: Related Workmentioning

confidence: 99%

“…Their system not only considers different emotional expressions, but also emotional versions of the robot’s utterances. In the work of Sönmez et al [ 42 ], the robot’s flow of emotions is affected by its mood and the user’s emotion.…”

Section: Related Workmentioning

confidence: 99%

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Creating Expressive Social Robots That Convey Symbolic and Spontaneous Communication

Fernández-Rodicio,

Castro-González,

Gamboa-Montero

et al. 2024

Sensors

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Robots are becoming an increasingly important part of our society and have started to be used in tasks that require communicating with humans. Communication can be decoupled in two dimensions: symbolic (information aimed to achieve a particular goal) and spontaneous (displaying the speaker’s emotional and motivational state) communication. Thus, to enhance human–robot interactions, the expressions that are used have to convey both dimensions. This paper presents a method for modelling a robot’s expressiveness as a combination of these two dimensions, where each of them can be generated independently. This is the first contribution of our work. The second contribution is the development of an expressiveness architecture that uses predefined multimodal expressions to convey the symbolic dimension and integrates a series of modulation strategies for conveying the robot’s mood and emotions. In order to validate the performance of the proposed architecture, the last contribution is a series of experiments that aim to study the effect that the addition of the spontaneous dimension of communication and its fusion with the symbolic dimension has on how people perceive a social robot. Our results show that the modulation strategies improve the users’ perception and can convey a recognizable affective state.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Creating Expressive Social Robots That Convey Symbolic and Spontaneous Communication

Fernández-Rodicio,

Castro-González,

Gamboa-Montero

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, most state-of-the-art models are static representations that do not include dynamics [20,21,22], i.e., do not mathematically describe the evolution of the mental states of humans in time. The articles that do present a dynamic model are scarce [39,19], and none of them proposes a general framework that covers the main aspects of ToM for a general context. For example, [39] dynamically models the emotions but does not focus on any other mental states, or on perception or decisionmaking, and [19] presents a dynamic model that is tailored to one specific context.…”

Section: Related Work and Open Challengesmentioning

confidence: 99%

“…The articles that do present a dynamic model are scarce [39,19], and none of them proposes a general framework that covers the main aspects of ToM for a general context. For example, [39] dynamically models the emotions but does not focus on any other mental states, or on perception or decisionmaking, and [19] presents a dynamic model that is tailored to one specific context. To the best of our knowledge, there has been no follow-up research on potential extensions for the model given in [30] in order to address its challenges and to exploit its potential for representing a ToM framework.…”

Section: Related Work and Open Challengesmentioning

confidence: 99%

Dynamic Mathematical Models of Theory of Mind for Socially Assistive Robots

Patrício,

Jamshidnejad

2023

IEEE Access

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Interactive machines should establish and maintain meaningful social interactions with humans. Thus, they need to understand and predict the mental states and actions of humans. Based on Theory of Mind (ToM), in order to understand and interact with each other, humans develop cognitive models of one another. Our main goal is to provide a mathematical framework based on ToM to improve the understanding of interactive machines regarding the perception, cognition, and decision-making of humans. Most stateof-the-art models of behavioral theories based on machine learning are focused on input-output black-box representations. Thus, they lack transparency and generalizability, and exhaustive training procedures are needed to personalize them for various humans. Moreover, these models lack dynamics, i.e., they do not mathematically describe the evolution of the mental states and actions of humans in time. Following a systems-and-control-theoretic point-of-view, we represent for the first time the perception, cognition, and decision-making of humans via a dynamic, mathematical framework by introducing a novel formalization and an extension to Fuzzy Cognitive Maps (FCMs). The resulting models are given in a general state-space representation, which can be used by interactive machines within known model-based state estimation and control methods. In a case study, the resulting models were identified and validated for 21 participants, in scenarios where predicting the intentions and behavior of the participants required understanding the dynamics of their mental procedures. The results of these experiments show that our model is capable of incorporating the dynamics to estimate the intentions and predict the behavior of the participants, with an accuracy of, respectively, 81.55% and 66.06%. Moreover, we compared our model with a state-of-the-art formalization of human cognition, which was made dynamic using our introduced FCM framework. Our model, which in addition to the elements of the state-of-the-art model included emotions, personality traits, and biases (thus providing a more transparent insight about the mental procedures of the participants) showed 6.25% and 2.45% more accuracy in, respectively, estimating the intentions and predicting the behavior of the participants.INDEX TERMS Dynamic mathematical models, long-term interactions of rational agents, state-space models of cognition, theory of mind.

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