Proximate human-robot teaming (pxHRT) is a complex subspace within human-robot interaction. Studies in this space involve a range of equipment and methods, including the ability to sense people and robots precisely. Research in this area draws from a wide variety of other fields, from human-human interaction to control theory, making the study design complex, particularly for those outside the field of HRI. In this paper, we introduce a framework that helps researchers consider tradeoffs across various task contexts, platforms, sensors, and analysis methods; metrics frequently used in the field; and common challenges researchers may face. We demonstrate the use of the framework via a case study which employs an autonomous mobile manipulator continuously engaging in shared workspace, handover, and co-manipulation tasks with people, and explores the effect of cognitive workload on pxHRT dynamics. We also demonstrate the utility of the framework in a case study with two groups of researchers new to pxHRT. With this framework, we hope to enable researchers, especially those outside HRI, to more thoroughly consider these complex components within their studies, more easily design experiments, and more fully explore research questions within the space of pxHRT.

As the first robotics journal offered by ACM, the Transactions on Human-Robot Interaction (ACM THRI) made considerable progress to advance the field of HRI since its inaugural offering in 2018.ACM THRI remains the flagship journal for leading research in the burgeoning area of Human-Robot Interaction (HRI).The interdisciplinary field of HRI aims to understand and advance our knowledge of human-robot systems towards advancing usable and useful robotic technology.ACM THRI features leading thought into how people interact with robots and robotic technologies, how to improve these interactions and make new kinds of interaction possible, and the effects of such interactions on organizations or society.HRI brings together researchers and practitioners with diverse expertise, including but not limited to robotics, artificial intelligence, human factors, psychology, design, haptics, and mechatronics.We officially began our term in January

This work addresses the problem of planning a robot configuration and grasp to position a shared object during forceful human-robot collaboration, such as a puncturing or a cutting task. Particularly, our goal is to find a robot configuration that positions the jointly manipulated object such that the muscular effort of the human, operating on the same object, is minimized while also ensuring the stability of the interaction for the robot. This raises three challenges. First, we predict the human muscular effort given a human-robot combined kinematic configuration and the interaction forces of a task. To do this, we perform task-space to muscle-space mapping for two different musculoskeletal models of the human arm. Second, we predict the human body kinematic configuration given a robot configuration and the resulting object pose in the workspace. To do this, we assume that the human prefers the body configuration that minimizes the muscular effort. And third, we ensure that, under the forces applied by the human, the robot grasp on the object is stable and the robot joint torques are within limits. Addressing these three challenges, we build a planner that, given a forceful task description, can output the robot grasp on an object and the robot configuration to position the shared object in space. We quantitatively analyze the performance of the planner and the validity of our assumptions. We conduct experiments with human subjects to measure their kinematic configurations, muscular activity, and force output during collaborative puncturing and cutting tasks. The results illustrate the effectiveness of our planner in reducing the human muscular load. For instance, for the puncturing task, our planner is able to reduce muscular load by \( 69.5\% \) compared to a user-based selection of object poses.

We tested the hypothesis that, if a robot apparently invests effort in teaching a new skill to a human participant, the human participant will reciprocate by investing more effort in teaching the robot a new skill, too. To this end, we devised a scenario in which the iCub and a human participant alternated in teaching each other new skills. In the Adaptive condition of the robot teaching phase , the iCub slowed down its movements when repeating a demonstration for the human learner, whereas in the Unadaptive condition it sped the movements up when repeating the demonstration. In a subsequent participant teaching phase , human participants were asked to give the iCub a demonstration, and then to repeat it if the iCub had not understood. We predicted that in the Adaptive condition , participants would reciprocate the iCub’s adaptivity by investing more effort to slow down their movements and to increase segmentation when repeating their demonstration. The results showed that this was true when participants experienced the Adaptive condition after the Unadaptive condition and not when the order was inverted, indicating that participants were particularly sensitive to the changes in the iCub’s level of commitment over the course of the experiment.

Design teams of social robots are often multidisciplinary, due to the broad knowledge from different scientific domains needed to develop such complex technology. However, tools to facilitate multidisciplinary collaboration are scarce. We introduce a framework for the participatory design of social robots and corresponding canvas tool for participatory design. The canvases can be applied in different parts of the design process to facilitate collaboration between experts of different fields, as well as to incorporate prospective users of the robot into the design process. We investigate the usability of the proposed canvases with two social robot design case studies: a robot that played games online with teenage users and a librarian robot that guided users at a public library. We observe through participants’ feedback that the canvases have the advantages of (1) providing structure, clarity, and a clear process to the design; (2) encouraging designers and users to share their viewpoints to progress toward a shared one; and (3) providing an educational and enjoyable design experience for the teams.

The application of anthropomorphic features to robots is generally considered beneficial for human-robot interaction (HRI ). Although previous research has mainly focused on social robots, the phenomenon gains increasing attention in industrial human-Robot interaction as well. In this study, the impact of anthropomorphic design of a collaborative industrial robot on the dynamics of trust and visual attention allocation was examined. Participants interacted with a robot, which was either anthropomorphically or non-anthropomorphically designed. Unexpectedly, attribute-based trust measures revealed no beneficial effect of anthropomorphism but even a negative impact on the perceived reliability of the robot. Trust behavior was not significantly affected by an anthropomorphic robot design during faultless interactions, but showed a relatively steeper decrease after participants experienced a failure of the robot. With regard to attention allocation, the study clearly reveals a distracting effect of anthropomorphic robot design. The results emphasize that anthropomorphism might not be an appropriate feature in industrial HRI as it not only failed to reveal positive effects on trust, but distracted participants from relevant task areas which might be a significant drawback with regard to occupational safety in HRI.

As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot’s ability to coordinate team activities improves based on its ability to infer and reason about the dynamic (i.e., the “learning curve”) and stochastic task performance of its human counterparts. We introduce a novel resource coordination algorithm that enables robots to schedule team activities by (1) actively characterizing the task performance of their human teammates and (2) ensuring the schedule is robust to temporal constraints given this characterization. We first validate our modeling assumptions via user study. From this user study, we create a data-driven prior distribution over human task performance for our virtual and physical evaluations of human-robot teaming. Second, we show that our methods are scalable and produce high-quality schedules. Third, we conduct a between-subjects experiment (n = 90) to assess the effects on a human-robot team of a robot scheduler actively exploring the humans’ task proficiency. Our results indicate that human-robot working alliance (\( p\lt 0.001 \)) and human performance (\( p=0.00359 \)) are maximized when the robot dedicates more time to exploring the capabilities of human teammates.

No abstract available.

Most people do not have direct access to knowledge about the inner workings of robots. Instead, they must develop mental models of the robot, a process that is not well understood. This article presents findings from a long-term, in-the-wild, qualitative, hypothesis-generating study of the mental model formation process. The focus was on how (qualitatively) users form mental models of the robot—specifically its perceptual capabilities, rules of behavior, and communication with other humans. Participants of diverse ages had multiple interactions with the robot over six weeks in a non-laboratory setting. The robot’s rules of behavior were changed every two weeks. A novel, non-anthropomorphic robot was created for the study with a realistic use case: storing people’s shoes during a yoga class. This article reports findings from a case study analysis of 28 interviews conducted over six weeks with six participants. These findings are organized into six topics: (1) variability in the rate at which mental models are updated to be more predictive, (2) types of reasoning and hypothesizing about the robot, (3) borrowing from existing mental models and use of imagination, (4) attributing sensing capabilities where there are no visible sensors, (5) judgments about whether the robot is autonomous or teleoperated, and (6) experimenting with the robot. Specific suggestions for future research are given throughout, culminating in a set of study design recommendations. This work demonstrates the fruitfulness of long-term, in-the-wild studies of human-robot interaction, of which mental model formation is a foundational aspect.

We propose an agent model that determines its behavior from an internal state and a spatial relationship with a target to generate approaching and avoiding behaviors in encounter scenes. This model is based on the relationship with an opponent rather than with a scenario. The agent moves to increase the utility value obtained from the preferences for both aggressive and passive involvement. We analyzed the behavioral and utterance data of human–human interactions based only on two-dimensional position information by simple-shaped robots. The rate of participants’ behavior following the model was significantly higher than that of a random walker. Based on this result, we estimated the internal state during the interactions from the behavior of the participants and analyzed it. The words uttered by one member of a pair correlated with the internal state estimated from the behavior of the other member of the pair. The frequency of the internal states observed from the participants who were recommended to interact with the partner was different from that observed from the participants who did not receive such recommendations. These results suggest that a model with two preferences can approximate a human’s internal state in encounter scenes.