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  1. Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users’ natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future. 
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    Free, publicly-accessible full text available March 11, 2025
  2. A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.

     
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    Free, publicly-accessible full text available February 9, 2025
  3. Social robots in the home will need to solve audio identification problems to better interact with their users. This paper focuses on the classification between a)naturalconversation that includes at least one co-located user and b)mediathat is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user’s social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments which contained eithernaturalormediaaudio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers, and assessed the classifiers’ abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and discuss the timing and size of the trained classifiers, as well as privacy and ethics considerations.

     
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    Free, publicly-accessible full text available August 18, 2024
  4. Free, publicly-accessible full text available August 1, 2024
  5. The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task. 
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  6. In this paper, we introduce Ommie, a novel robot that supports deep breathing practices for the purposes of anxiety reduction. The robot’s primary function is to guide users through a series of extended inhales, exhales, and holds by way of haptic interactions and audio cues. We present core design decisions during development, such as robot morphology and tactility, as well as the results of a usability study in collaboration with a local wellness center. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures, and participants found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. These results show promise in the robot’s capacity for supporting mental health. 
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  7. Robot-to-human handovers are common exercises in many robotics application domains. The requirements of handovers may vary across these different domains. In this paper, we first devised a taxonomy to organize the diverse and sometimes contradictory requirements. Among these, task-oriented handovers are not well-studied but important because the purpose of the handovers in human-robot collaboration (HRC) is not merely to pass an object from a robot to a human receiver, but to enable the receiver to use it in a subsequent tool-use task. A successful task-oriented handover should incorporate task-related information - orienting the tool such that the human can grasp it in a way that is suitable for the task. We identified multiple difficulty levels of task-oriented handovers, and implemented a system to generate handovers with novel tools on a physical robot. Unlike previous studies on task-oriented handovers, we trained the robot with tool-use demonstrations rather than handover demonstrations, since task-oriented handovers are dependent on the tool usages in the subsequent task. We demonstrated that our method can adapt to all difficulty levels of task-oriented handovers, including tasks that matched the typical usage of the tool, tasks that required an improvised or unusual usage of the tool, and tasks where the handover was adapted to the pose of a manipulandum. 
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  8. Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Colocated robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the colocated robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition. 
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