Reflections on the Design Challenges Prompted by Affect-Aware Socially Assistive Robots

Wilson, Jason R.; Scheutz, Matthias; Briggs, Gordon

doi:10.1007/978-3-319-31413-6_18

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Apart from discrimination, dehumanization, and deception, which represent phenomena that are potentially relevant for all types of robots involved in HRI, some authors suggest that there are specific ethical issues related to socially assistive robots (SAR), in particular (e.g., [73]). They propose that these issues are unique to SAR due to their more social nature compared with other types of robots.…”

Section: Hri-specific Ethical Challengesmentioning

confidence: 99%

“…SAR are defined as a class of robots between "assistive robotics (robots that provide assistance to a user) and socially interactive robotics (robots that communicate with a user through social and nonphysical interaction)" [74 , p. 25]. Wilson et al [73] suggest the following ethical issues are particularly relevant for social robots: A respect for social norms, the robot being able to make decisions about competing obligations, building and maintaining trust between robot and user, the potential problem of social manipulation and deception by the robot, and the issue of blame and justification, especially if something goes wrong [73]. As the task of building and maintaining trust between robots and users is an important ethical factor in the contexts of socially assistive robots [73], there are trust-based approaches to ethical social robots.…”

Section: Hri-specific Ethical Challengesmentioning

confidence: 99%

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Societal and Ethical Issues in HRI

Wullenkord

Eyssel

2020

Curr Robot Rep

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Purpose of the Review The purpose of this review is to give an overview of the societal and ethical issues in human-robot interaction, (HRI), mainly focusing on the literature of the last five years. Recent Findings Both general ethical challenges associated with robot deployment and those specific to human-robot interaction are addressed and complemented by discussions of ethics within HRI research, ethics related behavior towards robots, as well as ethics and robot rights. Moreover, we discuss ethical challenges in sensitive contexts such as medicine, rehabilitation, and care. We conclude our review by providing an overview of the key ethics frameworks and guidelines to inspire researchers, developers, and stakeholders alike. Summary This review offers a timely overview of the state-of-the art societal and ethical issues that arise from the ever more steady integration of robots into human society. We exemplify the key issues and debates in the field by mainly covering the literature of the past five years.

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Section: Hri-specific Ethical Challengesmentioning

confidence: 99%

Section: Hri-specific Ethical Challengesmentioning

confidence: 99%

Societal and Ethical Issues in HRI

Wullenkord

Eyssel

2020

Curr Robot Rep

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“…To achieve MU, two approaches are necessary; on the one hand, the robots should be able to demonstrate more readable and transparent behavior that would increase interpretability and anticipation by the human users (Wallkötter et al, 2021 ), and on the other hand, the robots should be equipped with high-level cognitive skills to interpret the human partner's global states (needs, intentions, emotional states) and react to them accordingly. Specifically, the robot's emotional system must be built both in terms of affective elicitation and sensing (Wilson et al, 2016 ). This can be accomplished by creating robots capable of eliciting emotions through humanized social actions and interpreting human emotions in the same way that a human partner would.…”

Section: Introductionmentioning

confidence: 99%

Classifying human emotions in HRI: applying global optimization model to EEG brain signals

Staffa,

D'Errico,

Sansalone

et al. 2023

Front. Neurorobot.

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Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future.

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“…Robotic deception has been an important topic in HRI [1]. Some researchers are concerned about the possible harmful impacts of deception in social robots [9][10][11]. One of the concerns is that users might overtrust a robot's capabilities and allow the robot to make unqualified decisions [9].…”

Section: Introductionmentioning

confidence: 99%

“…One of the concerns is that users might overtrust a robot's capabilities and allow the robot to make unqualified decisions [9]. Moreover, Wilson et al are concerned that robot deception may damage humanrobot trust and can even lead to manipulation, especially for aging high-risk populations [11].…”

Section: Introductionmentioning

confidence: 99%

Case-based robotic architecture with multiple underlying ethical frameworks for human-robot interaction

Chen¹,

Arkin²,

Borenstein³

et al. 2022

Value-Sharing Between Humans and Robots

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As robots are becoming more intelligent and more commonly used, it is critical for robots to behave ethically in human-robot interactions. However, there is a lack of agreement on a correct moral theory to guide human behavior, let alone robots. This paper introduces a robotic architecture that leverages cases drawn from different ethical frameworks to guide the ethical decision-making process and select the appropriate robotic action based on the specific situation. We also present an architecture implementation design used on a pill sorting task for older adults, where the robot needs to decide if it is appropriate to provide false encouragement so that the adults continue to be engaged in the training task.

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Reflections on the Design Challenges Prompted by Affect-Aware Socially Assistive Robots

Cited by 4 publications

References 18 publications

Societal and Ethical Issues in HRI

Societal and Ethical Issues in HRI

Classifying human emotions in HRI: applying global optimization model to EEG brain signals

Case-based robotic architecture with multiple underlying ethical frameworks for human-robot interaction

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