Human-robot Teams Research Articles

Subgroup Formation in Human-Robot Teams: A Multi-Study Mixed Method Approach with Implications for Theory and Practice

Subgroup Formation in Human-Robot Teams: A Multi-Study Mixed Method Approach with Implications for Theory and Practice

Evaluation of Robot Degradation on Human-Robot Collaborative Performance in Manufacturing.

Human-robot collaborative systems are highly sought candidates for smart manufacturing applications because of their adaptability and consistency in production tasks. However, manufacturers are still hesitant to adopt these systems because of the lack of metrics regarding the influence of the degradation of collaborative industrial robots on human-robot teaming performance. Hence, this paper defines teaming performance metrics with respect to robot degradation. In addition, the defined metrics are applied to a human-robot collaborative inverse peg-in-hole case study with respect to the degradation of the joint angular encoder and current sensor. Specifically, this case study compares pure insertion versus insertion with spatial scanning to solve the peg-in-hole problem, and manual intervention is implemented in the event of robotic failure. The metrics used in the case study showed that pure insertion more sensitive to robot degradation with manual intervention was required at 0.04° as opposed to 0.12° from insertion with scanning. Therefore, insertion with scanning was shown to be more robust to robot degradation at the cost of a slower insertion time of 9.48 s compared to 3.19 s. Thus, this paper provides knowledge and usable metrics regarding the influence of robot degradation on human-robot collaborative systems in manufacturing applications.

Subgroup Formation in Human-Robot Teams: A Multi-Study Mixed Method Approach with Implications for Theory and Practice

Subgroup Formation in Human-Robot Teams: A Multi-Study Mixed Method Approach with Implications for Theory and Practice

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Towards Human–Robot Teaming: Tradeoffs of Explanation-Based Communication Strategies in a Virtual Search and Rescue Task

Designing human-robot collaboration (HRC) workspaces in industrial settings: A systematic literature review

In the pursuit of increasing efficiency, productivity and flexibility at production lines and their corresponding workstations, manufacturing companies have started to heavily invest in “collaborative workspaces” where close interaction between humans and robots promises to lead to these goals that neither can achieve on their own. Therefore, it is necessary to know the contributions, recommendations and guidelines that literature presents in terms of designing a manufacturing workplace where humans and cobots interact with each other to accomplish the defined objectives. These aspects need to be explored in an integrated and multidisciplinary way to maximize human involvement in the decision chain and to promote wellbeing and quality of work. This paper presents a systematic literature review on designing human-robot collaboration (HRC) workspaces for humans and robots in industrial settings. The study involved 252 articles in international journals and conferences proceedings published till 2019. A detailed selection process led to including 65 articles to further analysis. A framework that represents the complexity levels of the influencing factors presented in human-robot interaction (HRI) contexts was developed for the content analysis. Based on this framework the guidelines and recommendations of the analysed articles are presented in three categories: Category 1 – the first level of complexity, which considers only one specific influencing factor in the HRI. This category was split into two: human operator, and technology; Category 2 – the second level of complexity, includes recommendations and guidelines related to human-robot team’s performance, and thus several influencing factors are present in the HRI; and, finally, Category 3 – the third level of complexity, where recommendations and guidelines for more complex and holistic approaches in the HRI are presented. The literature offers contributions from several knowledge areas capable to design safe, ergonomic, sustainable, and healthy human-centred workplaces where not only technical but also social and psychophysical aspects of collaboration are considered.

Coordinating Human-Robot Teams with Dynamic and Stochastic Task Proficiencies

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.

Influencing leading and following in human–robot teams

Influencing leading and following in human–robot teams

Real-time recognition of team behaviors by multisensory graph-embedded robot learning

Awareness of team behaviors (e.g., individual activities and team intents) plays a critical role in human–robot teaming. Autonomous robots need to be aware of the overall intent of the team they are collaborating with in order to effectively aid their human peers or augment the team’s capabilities. Team intents encode the goal of the team, which cannot be simply identified from a collection of individual activities. Instead, teammate relationships must also be encoded for team intent recognition. In this article, we introduce a novel representation learning approach to recognizing team intent awareness in real-time, based upon both individual human activities and the relationship between human peers in the team. Our approach formulates the task of robot learning for team intent recognition as a joint regularized optimization problem, which encodes individual activities as latent variables and represents teammate relationships through graph embedding. In addition, we design a new algorithm to efficiently solve the formulated regularized optimization problem, which possesses a theoretical guarantee to converge to the optimal solution. To evaluate our approach’s performance on team intent recognition, we test our approach on a public benchmark group activity dataset and a multisensory underground search and rescue team behavior dataset newly collected from robots in an underground environment, as well as perform a proof-of-concept case study on a physical robot. The experimental results have demonstrated both the superior accuracy of our proposed approach and its suitability for real-time applications on mobile robots.

A Remote Synthetic Testbed For Human-Robot Teaming: An Iterative Design Process

Urban Search and Rescue (USAR) missions often involve a need to complete tasks in hazardous environments. In such situations, human-robot teams (HRT) may be essential tools for future USAR missions. Transparency and explanation are two information exchange processes where transparency is real-time information exchange and explanation is not. For effective HRTs, certain levels of transparency and explanation must be met, but how can these modes of team communication be operationalized? During the COVID-19 pandemic, our approach to answering this question involved an iterative design process that factored in our research objectives as inputs and pilot studies with remote participants. Our final research testbed design resulted in converting an in-person task environment to a completely remote study and task environment. Changes to the study environment included: utilizing user-friendly video conferencing tools such as Zoom and a custom-built application for research administration tasks and improved modes of HRT communication that helped us avoid confounding our performance measures.

A Comparison of Auditory and Visual Representations of System Confidence to Support Trust Specificity, Attention Management, and Joint Performance in Human-Machine Teams

Breakdowns in human-robot teaming can result from trust miscalibration, i.e., a poor mapping of trust to a system’s capabilities, resulting in misuse or disuse of the technology. Trust miscalibration also negatively affects operators’ top-down attention allocation and monitoring of the system. This experiment assessed the efficacy of visual and auditory representations of a system’s confidence in its own abilities for supporting trust specificity, attention management and joint performance in the context of a UAV-supported target detection task. In contrast to earlier studies, neither visual nor auditory confidence information improved detection accuracy. Visual representations of confidence led to slower response times than auditory representations, likely due to resource competition with the visual target detection task. Finally, slower response times were observed when a UAV incorrectly detected a target. Results from this study can inform the design of visual and auditory representations of system confidence in human-machine teams with high attention demands.

Attributing Blame in Human-Robot Teams with Robots of Differing Appearance

As automation becomes increasingly common in daily life the importance of understanding how we interact with automated systems increases, especially attribution of blame for accidents involving a human-automation team. The current research project looks at how humans attribute blame in an accident involving a human operator and a robot worker and is based on a previous study (Furlough et al., 2019). Participants will read two scenarios detailing an accident while being shown both the operator and robot. The robot’s appearance is manipulated to imply varying levels of automation, ranging from a simple robot to one with a human appearance. Results showed no significant effects, however, this research still has the potential to contribute to the understanding of interactions between humans and automated systems and could inform design in the future to facilitate a positive working environment with robots and humans.

Empathy and Schadenfreude in Human-Robot Teams.

Intergroup dynamics shape the ways in which we interact with other people. We feel more empathy towards ingroup members compared to outgroup members, and can even feel pleasure when an outgroup member experiences misfortune, known as schadenfreude. Here, we test the extent to which these intergroup biases emerge during interactions with robots. We measured trial-by-trial fluctuations in emotional reactivity to the outcome of a competitive reaction time game to assess both empathy and schadenfreude in arbitrary human-human and human-robot teams. Across four experiments (total n = 361), we observed a consistent empathy and schadenfreude bias driven by team membership. People felt more empathy towards ingroup members than outgroup members and more schadenfreude towards outgroup members. The existence of an intergroup bias did not depend on the nature of the agent: the same effects were observed for human-human and human–robot teams. People reported similar levels of empathy and schadenfreude towards a human and robot player. The human likeness of the robot did not consistently influence this intergroup bias. In other words, similar empathy and schadenfreude biases were observed for both humanoid and mechanoid robots. For all teams, this bias was influenced by the level of team identification; individuals who identified more with their team showed stronger intergroup empathy and schadenfreude bias. Together, we show that similar intergroup dynamics that shape our interactions with people can also shape interactions with robots. Our results highlight the importance of taking intergroup biases into account when examining social dynamics of human-robot interactions.

Development and validation of the fluency in human-robot interaction scale. A two-wave study on three perspectives of fluency

Contemporary work offers many humans the possibility to collaborate with robots. In fact, in industrial settings, human-robot interaction has become a necessity. To improve the quality of human collaboration with their robotic counterparts, practitioners and researchers turn their attention to the concept of fluency. It is crucial for human-robot interaction yet difficult to investigate. Moreover, we argue that the notion of fluency being a unidimensional construct is insufficient to understand the complex interaction in a human-robot team. Therefore, we aimed to develop and validate a new measure to study subjective fluency from three different perspectives, i.e., the human-oriented, the robot-oriented, and the team-oriented. The scale can serve as a tool to better understand the dynamics of the relationship between the operator and the robot.In this paper, we presented the factor analyses and psychometric properties of the Fluency in Human-Robot Interaction Scale. Furthermore, we tested the validity of the scale by investigating the relationship between three perspectives of fluency and the individual performance as well as fluency change over 6 weeks. The results showed positive relationships among all perspectives of fluency and performance. However, no change over time was observed. The findings were discussed with regard to the possible use of the scale in practical and academic contexts.

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors.

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning.

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

Reverse Psychology in Trust-Aware Human-Robot Interaction

To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately “manipulate” the human's trust under the reverse psychology model. To correct this “manipulative” behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance.

A Unified Bi-Directional Model for Natural and Artificial Trust in Human–Robot Collaboration

We introduce a novel capabilities-based bi-directional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities. Trustee agents' capabilities are not deterministic; they are represented by belief distributions. For each task to be executed, a higher level of trust is assigned to trustee agents who have demonstrated that their capabilities exceed the task's requirements. We report results of an online experiment with 284 participants, revealing that our model outperforms existing models for multi-task trust prediction from a human trustor. We also present simulations of the model for determining trust from a robotic trustor. Our model is useful for control authority allocation applications that involve human-robot teams.

Robot Authority in Human-Robot Teaming: Effects of Human-Likeness and Physical Embodiment on Compliance.

The anticipated social capabilities of robots may allow them to serve in authority roles as part of human-machine teams. To date, it is unclear if, and to what extent, human team members will comply with requests from their robotic teammates, and how such compliance compares to requests from human teammates. This research examined how the human-likeness and physical embodiment of a robot affect compliance to a robot's request to perseverate utilizing a novel task paradigm. Across a set of two studies, participants performed a visual search task while receiving ambiguous performance feedback. Compliance was evaluated when the participant requested to stop the task and the coach urged the participant to keep practicing multiple times. In the first study, the coach was either physically co-located with the participant or located remotely via a live-video. Coach type varied in human-likeness and included either a real human (confederate), a Nao robot, or a modified Roomba robot. The second study expanded on the first by including a Baxter robot as a coach and replicated the findings in a different sample population with a strict chain of command culture. Results from both studies showed that participants comply with the requests of a robot for up to 11 min. Compliance is less than to a human and embodiment and human-likeness on had weak effects on compliance.

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

Mapping Meaning and Purpose in Human-Robot Teams: Anthropomorphic Agents in Military Operations

We spontaneously tend to project animacy and sensitivity to inanimate objects and sometimes we attribute distinctively human features like intelligence, goals, and reasons to certain artificial devices. This phenomenon is called “anthropomorphism” and has been long studied by researchers in human-robot interaction and social-robotics. These studies are particularly important from the perspective of recent developments in military technology, as autonomous systems controlled by AI are expected to play a greater and greater role in the future of warfare. Anthropomorphistic effects can play a critical role in tactical operations involving hybrid human-robot teams, where service members and autonomous agents need to quickly coordinate relying almost exclusively on fast, cognitively parsimonious, natural forms of communication. These forms rely importantly on anthropomorphism to allow human soldiers read the behavior of machines in terms of goals and intentions. Understanding the cognitive mechanisms that underpin anthropomorphistic attributions is hence potentially crucial to increase the accuracy and efficacy of human-machine interaction in military operations. However, this question is largely philosophical, as numerous models compete in the space of social cognition theory to explain behavior reading and mental states attribution. This paper aims to offer an initial exploration of these mechanisms from a perspective of philosophical psychology and cognitive philosophy, reviewing the theories in social cognition that are most promising to explain anthropomorphism and predict how it can enable and improve natural communication between soldiers and autonomous military technologies.