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1. A survey of communicating robot learning during human-robot interaction

   https://doi.org/10.1177/02783649241281369  

   Habibian, Soheil; Alvarez_Valdivia, Antonio; Blumenschein, Laura_H; Losey, Dylan_P (October 2024, The International Journal of Robotics Research)

   For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior. 

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2. What Happens When Robots Punish? Evaluating Human Task Performance During Robot-Initiated Punishment

   https://doi.org/10.1145/3472207  

   Jois, Himavath; Wagner, Alan R. (December 2021, ACM Transactions on Human-Robot Interaction)

   This article examines how people respond to robot-administered verbal and physical punishments. Human participants were tasked with sorting colored chips under time pressure and were punished by a robot when they made mistakes, such as inaccurate sorting or sorting too slowly. Participants were either punished verbally by being told to stop sorting for a fixed time, or physically, by restraining their ability to sort with an in-house crafted robotic exoskeleton. Either a human experimenter or the robot exoskeleton administered punishments, with participant task performance and subjective perceptions of their interaction with the robot recorded. The results indicate that participants made more mistakes on the task when under the threat of robot-administered punishment. Participants also tended to comply with robot-administered punishments at a lesser rate than human-administered punishments, which suggests that humans may not afford a robot the social authority to administer punishments. This study also contributes to our understanding of compliance with a robot and whether people accept a robot’s authority to punish. The results may influence the design of robots placed in authoritative roles and promote discussion of the ethical ramifications of robot-administered punishment. 

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3. Perceived Morality of Robot and Human Transgressors Varies By Perceived Ability to Feel

   Georgiou, Nicholas; Flanagan, Theresa; Scassellati, Brian; Kushnir, Tamar (March 2025, ACM)

   Mistakes, failures, and transgressions committed by a robot are inevitable as robots become more involved in our society. When a wrong behavior occurs, it is important to understand what factors might affect how the robot is perceived by people. In this paper, we investigated how the type of transgressor (human or robot) and type of backstory depicting the transgressor's mental capabilities (default, physio-emotional, socio-emotional, or cognitive) shaped participants' perceptions of the transgressor's morality. We performed an online, between-subjects study in which participants (N=720) were first introduced to the transgressor and its backstory, and then viewed a video of a real-life robot or human pushing down a human. Although participants attributed similarly high intent to both the robot and the human, the human was generally perceived to have higher morality than the robot. However, the backstory that was told about the transgressors' capabilities affected their perceived morality. We found that robots with emotional backstories (i.e., physio-emotional or socio-emotional) had higher perceived moral knowledge, emotional knowledge, and desire than other robots. We also found that humans with cognitive backstories were perceived with less emotional and moral knowledge than other humans. Our findings have consequences for robot ethics and robot design for HRI. 

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4. Investigating Robot Moral Advice to Deter Cheating Behavior

   Kim, Boyoung; Wen, Ruchen; de Visser, Ewart J.; Zhu, Qin; Williams, Tom; Phillips, Elizabeth (January 2021, TSAR Workshop at ROMAN 2021)

   null (Ed.)

   We examined whether a robot that proactively offers moral advice promoting the norm of honesty can discourage people from cheating. Participants were presented with an opportunity to cheat in a die-rolling game. Prior to playing the game, participants received from either a NAO robot or a human, a piece of moral advice grounded in either deontological, virtue, or Confucian role ethics, or did not receive any advice. We found that moral advice grounded in Confucian role ethics could reduce cheating when the advice was delivered by a human. No advice was effective when a robot delivered moral advice. These findings highlight challenges in building robots that can possibly guide people to follow moral norms. 

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5. Robots as Moral Advisors: The Effects of Deontological, Virtue, and Confucian Role Ethics on Encouraging Honest Behavior

   https://doi.org/10.1145/3434074.3446908  

   Kim, Boyoung; Wen, Ruchen; Zhu, Qin; Williams, Tom; Phillips, Elizabeth (March 2021, ACM/IEEE International Conference on Human-Robot Interaction)

   null (Ed.)

   We examined how robots can successfully serve as moral advisors for humans. We evaluated the effectiveness of moral advice grounded in deontological, virtue, and Confucian role ethics frameworks in encouraging humans to make honest decisions. Participants were introduced to a tempting situation where extra monetary gain could be earned by choosing to cheat (i.e., violating the norm of honesty). Prior to their decision, a robot encouraged honest choices by offering a piece of moral advice grounded in one of the three ethics frameworks. While the robot’s advice was overall not effective at discouraging dishonest choices, there was preliminary evidence indicating the relative effectiveness of moral advice drawn from deontology. We also explored how different cultural orientations (i.e., vertical and horizontal collectivism and individualism) influence honest decisions across differentially-framed moral advice. We found that individuals with a strong cultural orientation of establishing their own power and status through competition (i.e., high vertical individualism) were more likely to make dishonest choices, especially when moral advice was drawn from virtue ethics. Our findings suggest the importance of considering different ethical frameworks and cultural differences to design robots that can guide humans to comply with the norm of honesty. 

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