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1. Learning and Comfort in Human–Robot Interaction: A Review

   https://doi.org/10.3390/app9235152  

   Wang, Weitian; Chen, Yi; Li, Rui; Jia, Yunyi (December 2019, Applied Sciences)

   Collaborative robots provide prospective and great solutions to human–robot cooperative tasks. In this paper, we present a comprehensive review for two significant topics in human–robot interaction: robots learning from demonstrations and human comfort. The collaboration quality between the human and the robot has been improved largely by taking advantage of robots learning from demonstrations. Human teaching and robot learning approaches with their corresponding applications are investigated in this review. We also discuss several important issues that need to be paid attention to and addressed in the human–robot teaching–learning process. After that, the factors that may affect human comfort in human–robot interaction are described and discussed. Moreover, the measures utilized to improve human acceptance of robots and human comfort in human–robot interaction are also presented and discussed. 

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2. Case-based Robotic Architecture with Multiple Underlying Ethical Frameworks for Human-Robot Interaction

   Chen, S.; Arkin, R.; Borenstein, J.; Wagner, A. (July 2022, 7th International Conference on Robot Ethics and Standards)

   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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3. 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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4. Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue

   Das, Ranjini; Pon-Barry, Heather (July 2018, Proceedings of the SIGDIAL 2018 Conference)

   In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue. 

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5. Exploring Human Compliance Toward a Package Delivery Robot

   https://doi.org/10.1109/ICHMS56717.2022.9980806  

   Washburn, Andrew; Shrestha, Kripash; Ahmed, Habib; Feil-Seifer, Dave; La, Hung Manh (November 2022, IEEE)

   Human-robot interaction (HRI) studies have found people overtrust robots in domestic settings, even when the robot exhibits faulty behavior. Cognitive dissonance and selective attention explain these results. To test these theories, a novel HRI study was performed in a university library where participants were recruited to follow a package delivery robot. Participants then faced a dilemma to deliver a package in a private common room that might be off-limits. Then, they faced another dilemma when the robot stopped in front of an Emergency Exit door, and they had to trust the robot whether to open it or not Results showed individuals did not overtrust the robot and open the Emergency Exit door. Interestingly, most individuals demurred from entering the private common room when packages were not labeled, whereas groups of friends were more likely to enter the room. Then, selective attention was demonstrated by stopping participants in front of a similar Emergency Exit door and assessing whether they noticed it In one condition, only half of participants noticed it, and when the robot became more engaging no one noticed it. Additionally, a malfunctioning robot is exhibited, showing what kind of negative outcome was required to reduce trust. 

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