Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Alimardani, Maryam; Hiraki, Kei

doi:10.3389/frobt.2020.00125

Cited by 45 publications

(32 citation statements)

References 133 publications

(176 reference statements)

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“…In relation with the previous work, this research does not deviate from the facts that are listed in [60], which considers the EEG technology as a solution for training the users, enhancing the interaction with the robots and providing better assessment of the users. In fact, this research provides deeper insights on the challenges and the advantages of emotion-based application for controlling the robots as it increases the trustworthy between human and robots.…”

Section: Resultsmentioning

confidence: 79%

“…As a reflection of the importance that is earning emotion recognition for HRI, more emotion-driven applications have been developed. Some of the most relevant examples that can be found nowadays are: [60] presents a deep analysis in the future potential of using EEG analysis for enhancing humanrobot interactions. In the presented paper, the authors list the advantages of such an approach: providing a socially engaging training for the users, decreasing the time for calibration and configuration of the robots, and providing systematically viable solution for users' performance assessment while working with robots.…”

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confidence: 99%

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Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Eyam

Mohammed

Lastra

2021

Sensors

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The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

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Section: Resultsmentioning

confidence: 79%

mentioning

confidence: 99%

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Eyam

Mohammed

Lastra

2021

Sensors

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“…Kim et al ( 2017 ), for instance, use error-related potentials from EEG signals, for implicit feedback, to improve gesture-based robot control during human robot interactions. A recent review provides details as to how brain computer interfaces and neurofeedback research is now being used to estimate cognitive load, attentional level, perceived errors and emotions from brain signals to improve interactions between humans and robots (Alimardani and Hiraki, 2020 ). Zeng et al ( 2020 ) propose a brain inspired model of belief ToM using high-level knowledge of the functions of different brain regions relevant for ToM and test it on two simple false belief tasks.…”

Section: Cognitive Vs Computational Tom: a Discussionmentioning

confidence: 99%

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review

Langley

Cirstea

Cuzzolin

et al. 2022

Front. Artif. Intell.

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Theory of Mind (ToM)—the ability of the human mind to attribute mental states to others—is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.

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“…electroencephalography). 42 In the future, we will take into consideration one or some PwD’s cognitive and affective states as additional dimension(s) of state space, by integrating sensing technologies in a cost-effective way (e.g. in terms of computational complexity).…”

Section: Limitation and Future Workmentioning

confidence: 99%

A simulated experiment to explore robotic dialogue strategies for people with dementia

Yuan

Sadovnik

Zhang

et al. 2022

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction Persons with dementia (PwDs) often show symptoms of repetitive questioning, which brings great burdens on caregivers. Conversational robots hold promise of helping cope with PwDs’ repetitive behavior. This paper develops an adaptive conversation strategy to answer PwDs’ repetitive questions, follow up with new questions to distract PwDs from repetitive behavior, and stimulate their conversation and cognition. Methods We propose a general reinforcement learning model to interact with PwDs with repetitive questioning. Q-learning is exploited to learn adaptive conversation strategy (from the perspectives of rate and difficulty level of follow-up questions) for four simulated PwDs. A demonstration is presented using a humanoid robot. Results The designed Q-learning model performs better than random action selection model. The RL-based conversation strategy is adaptive to PwDs with different cognitive capabilities and engagement levels. In the demonstration, the robot can answer a user’s repetitive questions and further come up with a follow-up question to engage the user in continuous conversations. Conclusions The designed Q-learning model demonstrates noteworthy effectiveness in adaptive action selection. This may provide some insights towards developing conversational social robots to cope with repetitive questioning by PwDs and increase their quality of life.

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Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Cited by 45 publications

References 133 publications

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review

A simulated experiment to explore robotic dialogue strategies for people with dementia

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