Human-robot Teams Research Articles

Impact of Transparency and Explanations on Trust and Situation Awareness in Human–Robot Teams

Urban Search and Rescue (USAR) missions continue to benefit from the incorporation of human–robot teams (HRTs). USAR environments can be ambiguous, hazardous, and unstable. The integration of robot teammates into USAR missions has enabled human teammates to access areas of uncertainty, including hazardous locations. For HRTs to be effective, it is pertinent to understand the factors that influence team effectiveness, such as having shared goals, mutual understanding, and efficient communication. The purpose of our research is to determine how to (1) better establish human trust, (2) identify useful levels of robot transparency and robot explanations, (3) ensure situation awareness, and (4) encourage a bipartisan role amongst teammates. By implementing robot transparency and robot explanations, we found that the driving factors for effective HRTs rely on robot explanations that are context-driven and are readily available to the human teammate.

Human Robot Interaction - key areas in teamwork.

Industry 4.0 is based on the implementation of smart technologies. Work teams consisting of humans and intelligent machines, such as robots, are becoming more common. Their cooperation is based on joint performance of tasks in order to achieve set goals. The aim of the article is to present the challenges associated with the functioning of new types of work teams in organizations: based on cooperation between people and intelligent machines (robots or artificial intelligence algorithms). The article discusses the impact of factors such as trust, mental models, human personality, and machine reliability on the functioning of human-robot teams. It is shown how these areas can affect the performance of HRI - Human Robot Interaction Teams. The literature analysis indicates that before valuable human-robot interaction can occur, humans must first build trust in the machines. And the reliability of robots, and the human sense of control over them, increases trust. In addition, human personality and the attributes presented by a robot are also important for cooperation. For example, robots perceived as extroverted and socially intelligent have a high level of acceptance from humans.

The effect of cognitive workload on decision authority assignment in human–robot collaboration

Human–robot collaboration in dynamic industrial environments warrants robot flexibility and shifting between tasks. Adaptive robot behavior unavoidably carries decision-making needs regarding task allocation and scheduling. Such decisions can be made either by the human team members or autonomously, by the robot’s controlling algorithm. Human authority may help preserve situational awareness but increases mental demands due to increased responsibilities. Conversely, granting authority to the robot can offload the operator, at the cost of reduced intervention readiness. This paper aims to investigate the question of decision authority assignment in a human–robot team, in terms of performance, perceived workload and subjective preference. We hypothesized that the answer is influenced by the cognitive workload imposed on the human operator by the work process. An experiment with 21 participants was conducted, in which decision authority and induced workload through a secondary task were varied between trials. Results confirmed that operators can support the robot better when decision authority is allocated according to their workload. However, operator decision authority (a) may cause inferior performance at any secondary tasks performed in parallel with robot supervision and (b) increases perceived workload. Subjective preference was found to be evenly divided between the two levels of decision authority, and unaffected by task difficulty. In brief, if human–robot team performance is a priority, humans should be granted decision authority when their overall workload allows it. In high-workload conditions, system decision-making algorithms should be developed. Nonetheless, process designers should be mindful of the interpersonal differences between operators who are destined to collaborate with robots.

I Know I Am, But What Are You? How Culture and Self-Categorization Affect Emotions Toward Robots

As routinely working with robots spreads globally, it becomes important to understand how best to customize robots to each culture to maximize collaboration between humans and robots. In two distinct cultures (United States and Portugal) we examined group-based emotions toward robots with participants self-categorizing three different ways (ingroup, outgroup, and neutral). We tested and confirmed our baseline assumptions that Portuguese participants are more collectivistic and less individualistic, and feel closer with a team in negative, but not positive, scenarios, compared to United States participants. Supporting our hypotheses, the results showed that participants rated more positive emotions toward the robot in the ingroup condition than in the outgroup or neutral conditions. Moreover, an interaction effect between culture and self-categorization revealed that Portuguese participants had more positive group-based emotions toward the robot than United States participants when self-categorizing as an ingroup. We discuss the implications in terms of human-robot teaming and potential future research directions.

Ergonomics in construction robotics and human-robot teams in the AEC domain: a review

Over the last decade, the potential and prospects for using robotics for various construction activities have increased, particularly for dangerous work areas such as roof construction, construction in hazardous environments, and improving productivity while shielding construction workers from strenuous work and accidents. As there have been concerns about human factors in collaborating robotics with humans on construction worksites, ergonomics in human-robot teams’ research is critical to enhancing the advantages and adoption of collaborative robots in improving the productivity of construction workers and the competitiveness of construction organisations. This study reviews the emerging trends in human-robot teams and ergonomics in robotics, focusing on addressing the grey areas in human-robot teams’ body of knowledge. A systematic review of publications from similar industries with extensive studies on human-robot teams and factors applicable to the built environment were identified. The study summarises articles that have emerged over the last decade and highlights the emerging nature of robotics collaboration, ergonomic development and the interplay between robotics design and construction robotics ergonomics. Its outcome benefits AEC research and practice in building knowledge in construction human-robot collaboration, guiding practice and design in robotics by focusing on critical ergonomic issues.

The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams.

Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions. In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork. This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer. When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human-robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human-robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust. Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust. The study can help to better understand the interaction mechanism of the human-robot team and can better serve the management and cooperation of the human-robot team by appropriately adjusting the robots' fairness and social status.

A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion

This article studies rational and persistent deception among intelligent robots to enhance security and operational efficiency. We present an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -player <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -stage game with an asymmetric information structure where each robot’s private information is modeled as a random variable or its type. The deception is persistent as each robot’s private type remains unknown to other robots for all stages. The deception is rational as robots aim to achieve their deception goals at minimum cost. Each robot forms a dynamic belief of others’ types based on intrinsic or extrinsic information. Perfect Bayesian Nash equilibrium (PBNE) is a natural solution concept for dynamic games of incomplete information. Due to its requirements of sequential rationality and belief consistency, PBNE provides a reliable prediction of players’ actions, beliefs, and expected cumulative costs over the entire <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> stages. The contribution of this work is fourfold. First, we identify the PBNE computation as a nonlinear stochastic control problem and characterize the structures of players’ actions and costs under PBNE. We further derive a set of extended Riccati equations with cognitive coupling under the linear-quadratic (LQ) setting and extrinsic belief dynamics. Second, we develop a receding-horizon algorithm with low temporal and spatial complexity to compute PBNE under intrinsic belief dynamics. Third, we investigate a deceptive pursuit-evasion game as a case study and use numerical experiments to corroborate the results. Finally, we propose metrics, such as deceivability, reachability, and the price of deception (PoD), to evaluate the strategy design and the system performance under deception. Note to Practitioners—Recent advances in automation and adaptive control in multi-agent systems enable robots to use deception to accomplish their objectives. Deception involves intentional information hiding to compromise the security and operational efficiency of the robotic systems. This work proposes a dynamic game framework to quantify the impact of deception, understand the robots’ behaviors and intentions, and design cost-efficient strategies under the deception that persists over stages. Existing research studies on robot deception have relied on experiments while this work aims to lay a theoretical foundation of deception with quantitative metrics, such as deceivability and the PoD. The proposed model has wide applications, including cooperative robots, pursuit and evasion, and human–robot teaming. The pursuit-evasion games are used as case studies to show how the deceiver can amplify the deception by belief manipulation and how the deceived robots can reduce the negative impact of deception by enhanced maneuverability and Bayesian learning. The future work would focus on designing cooperative deception among swarm robotics and robotic systems that are robust to or further benefit from the deception.

Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

In this paper, we present a framework for trust-aware sequential decision-making in a human-robot team wherein the human agent’s trust in the robotic agent is dependent on the reward obtained by the team. We model the problem as a finite-horizon Markov Decision Process with the trust of the human on the robot as a state variable. We develop a reward-based performance metric to drive the trust update model, allowing the robotic agent to make trust-aware recommendations. We conduct a human-subject experiment with a total of 45 participants and analyze how the human agent’s trust evolves over time. Results show that the proposed trust update model is able to accurately capture the human agent’s trust dynamics. Moreover, we cluster the participants’ trust dynamics into three categories, namely, Bayesian decision makers, oscillators, and disbelievers, and identify personal characteristics that could be used to predict which type of trust dynamics a person will belong to. We find that the disbelievers are less extroverted, less agreeable, and have lower expectations toward the robotic agent, compared to the Bayesian decision makers and oscillators. The oscillators tend to get significantly more frustrated than the Bayesian decision makers.

Decentralized Control of a Heterogeneous Human–Robot Team for Exploration and Patrolling

We present a decentralized connectivity-maintenance control framework for a heterogeneous human–robot team. The algorithm is able to manage a team composed of an arbitrary number of mobile robots (drones and ground robots in our case) and humans, for collaboratively achieving exploration and patrolling tasks. Differently from other works on the subject, here the human user physically becomes part of the team, moving in the same environment of the robots and receiving information about the team connectivity through wearable haptics or audio feedback. Although human explores the environment, robots move so as to keep the team connected via a connectivity-maintenance algorithm; at the same time, each robot can also be assigned with a specific target to visit. We carried out three human subject experiments, both in virtual and real environments. Results show that the proposed approach is effective in a wide range of scenarios. Moreover, providing either haptic or audio feedback for conveying information about the team connectivity significantly improves the performance of the considered tasks, although users significantly preferred receiving haptic stimuli w.r.t. the audio ones. Note to Practitioners—Exploration, patrolling, and search-and-rescue are highly dynamic and unstructured scenarios. When considering the operative conditions of such environments, the benefits of multirobot systems are evident. Most tasks can be carried out faster and more robustly by a team of robots with respect to a single unit. There are also situations explicitly requiring the presence of a multirobot team, e.g., using one drone for surveillance of the ground team and one ground mobile robot for carrying supplies. Of course, if the operator(s) in charge of the operation could share the same environment of the robots (i.e., be together with the robots in the field), they would be provided with a level of situational awareness that no teleoperation technology can match as of today. This work presents a framework for controlling heterogeneous teams composed of one human operator and an arbitrary number of aerial and ground mobile robots. The operator moves together with the robotic team and, at the same time, he or she receives meaningful information about the status of the formation. The algorithm only uses the relative position of the drones and humans with respect to each other, and all computations are designed in a decentralized fashion. Decentralization avoids relying on any absolute positioning system (e.g., GPS) or centralized command centers. These features make the proposed framework ready for deployment in different high-impact applications, such as in surveillance, search-and-rescue, and disaster response scenarios.

Dynamic Task Scheduling for Human-Robot Collaboration

In human-robot collaboration systems, the human-robot team works together to complete all tasks of the assigned job. Tasks’ precedence order is a factor that determines which task should be completed first. A human agent has an expected performance on each task, but the actual performance of the human agent on a task may differ from the expected performance at different times. Therefore, it is also necessary to monitor the human agent so that the task can be transferred to or from the robot when the actual performance of the human agent is worse or better than the expected performance. We propose an architecture for dynamic task allocation and scheduling in a collaborative system. The method is to first identify the group of tasks that should be executed first based on the precedence order of tasks. After obtaining this group of prioritized tasks, we use the quality metric (a numerical value that indicates which agent is better at completing the task) to determine which agent should be assigned each of the tasks. Finally, real-time human agent monitoring is used to detect performance changes in the human agent so that tasks can be transferred from one agent to another. All of these processes are performed automatically without the need for a human agent to schedule or reschedule. We have validated the effectiveness of the architecture through several experiments.

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

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

Heterogeneous human–robot task allocation based on artificial trust

Effective human–robot collaboration requires the appropriate allocation of indivisible tasks between humans and robots. A task allocation method that appropriately makes use of the unique capabilities of each agent (either a human or a robot) can improve team performance. This paper presents a novel task allocation method for heterogeneous human–robot teams based on artificial trust from a robot that can learn agent capabilities over time and allocate both existing and novel tasks. Tasks are allocated to the agent that maximizes the expected total reward. The expected total reward incorporates trust in the agent to successfully execute the task as well as the task reward and cost associated with using that agent for that task. Trust in an agent is computed from an artificial trust model, where trust is assessed along a capability dimension by comparing the belief in agent capabilities with the task requirements. An agent’s capabilities are represented by a belief distribution and learned using stochastic task outcomes. Our task allocation method was simulated for a human–robot dyad. The team total reward of our artificial trust-based task allocation method outperforms other methods both when the human’s capabilities are initially unknown and when the human’s capabilities belief distribution has converged to the human’s actual capabilities. Our task allocation method enables human–robot teams to maximize their joint performance.

The influence of interdependence and a transparent or explainable communication style on human-robot teamwork.

Humans and robots are increasingly working together in human-robot teams. Teamwork requires communication, especially when interdependence between team members is high. In previous work, we identified a conceptual difference between sharing what you are doing (i.e., being transparent) and why you are doing it (i.e., being explainable). Although the second might sound better, it is important to avoid information overload. Therefore, an online experiment (n = 72) was conducted to study the effect of communication style of a robot (silent, transparent, explainable, or adaptive based on time pressure and relevancy) on human-robot teamwork. We examined the effects of these communication styles on trust in the robot, workload during the task, situation awareness, reliance on the robot, human contribution during the task, human communication frequency, and team performance. Moreover, we included two levels of interdependence between human and robot (high vs. low), since mutual dependency might influence which communication style is best. Participants collaborated with a virtual robot during two simulated search and rescue tasks varying in their level of interdependence. Results confirm that in general robot communication results in more trust in and understanding of the robot, while showing no evidence of a higher workload when the robot communicates or adds explanations to being transparent. Providing explanations, however, did result in more reliance on RescueBot. Furthermore, compared to being silent, only being explainable results a higher situation awareness when interdependence is high. Results further show that being highly interdependent decreases trust, reliance, and team performance while increasing workload and situation awareness. High interdependence also increases human communication if the robot is not silent, human rescue contribution if the robot does not provide explanations, and the strength of the positive association between situation awareness and team performance. From these results, we can conclude that robot communication is crucial for human-robot teamwork, and that important differences exist between being transparent, explainable, or adaptive. Our findings also highlight the fundamental importance of interdependence in studies on explainability in robots.

Metrics for Human-Robot Team Design: A Teamwork Perspective on Evaluation of Human-Robot Teams

Metrics for human-robot teaming should extend to teams consisting of multiple human and robotic agents, and to teams working in complex, dynamic work domains. This work proposes that to comprehensively analyze and evaluate multi-human, multi-robot teams, traditional HRI metrics of performance, and efficiency must be expanded upon to incorporate metrics of teamwork. We develop five distinct metrics to capture both ecological and cognitive aspects of teamwork found to be important in human-automation interaction, inspired by research in the cognitive systems engineering (CSE) community. We demonstrate the application of these metrics in a spacecraft maintenance case study comparing multiple human-robot team architectures. The case study demonstrates that the teamwork metrics capture aspects of human-robot interaction (HRI) not apparent when using only traditional performance and efficiency metrics. The article concludes that the proposed teamwork metrics are complementary to existing metrics in HRI and should be included in the evaluation of human-robot teams.

Introduction to the Special Issue on Test Methods for Human-Robot Teaming Performance Evaluations

This special issue of the Transactions on Human-Robot Interaction highlights, documents, and explores the metrics, test methods, and artifacts used in human-robot interaction (HRI) research. This collection of articles brings to attention the commonalities between the application of measurement science for the assessment and assurance of human-centric robotics in a variety of application domains, including industry, education, and defense. This special issue draws specific attention to the use and impact of metrology toward the advancement of HRI technologies and algorithms, and it promotes the application of measurement science toward the benchmarking and replication of HRI research. Special attention is given to the use cases, data sets, test methodologies, measurement techniques, metrics, and statistical analyses used to evaluate system performance.

Effects of Information Modality and Confidence on Trust and Situation Awareness in Human-Robot Teaming

Effects of Information Modality and Confidence on Trust and Situation Awareness in Human-Robot Teaming

Human-Robot Teams: A Discussion of the Emerging Trends

As technology advances, human robot-teams are becoming increasingly commonplace with team interactions taking place in a variety of military, industrial, service, and social settings. Consequently, as HR teams evolve, novel and unique considerations for how these teams interact and perform are rising. The purpose of this panel, therefore, is to host a discussion on state-of-theart issues and emerging trends that practitioners and researchers should scrutinize as the applications of HR teams expands. To provide a diverse set of perspectives, our panelists hail from a variety of backgrounds: military, industry, and academy. Talks will focus on moral and ethical considerations for human-agent decision-making, the influence of robots on team processes in human surgical teams, and on new considerations for trust in human-agent teams, including for covert measures of trust and for the inclusion of team cohesion in trust measurement.

Maybe I Can Do Better Myself: The Impact of Automation Levels on HRI

Although previous studies have developed scales to measure levels of robot autonomy and ways to quantify trust in human-robot teams, there is still much interest in determining how trust, individual differences, and levels of autonomy impact these teams. The primary objective of this study is to identify how pre-existing attitudes about robots, individual differences in normal cognition, and levels of robot autonomy affect performance, communication, and trust with a robot teammate. This study pairs participants with a robot teammate to compete against two robotic opponents in a simulated game of capture the flag. Game performance and interactions with the robot teammate will be collected as outcome measures. Subjective measures will include pre-existing negative attitudes toward robots (Nomura et al., 2006a) and trust (Schaefer, 2016).

Trailblazing Roblox Virtual Synthetic Testbed Development for Human-Robot Teaming Studies

Virtual testbeds are fundamental to the success of research on cognitive work in safety-critical domains. A testbed that can meet researchers' objectives and create a sense of reality for participants positively impacts the research process; they have the potential to allow researchers to address questions not achievable in physical environments. This paper discusses the development of a synthetic task environment (STE) for Urban Search and Rescue (USAR) to advance the boundaries of Human-Robot Teams (HRTs) using Roblox. Virtual testbeds can simulate USAR task environments and HRT interactions. After assessing alternative STE platforms, we discovered Roblox not only met our research capabilities but also would prove invaluable for research teams without substantial coding experience. This paper outlines the design process of creating an STE to meet our research team's objectives.

Advancing Human-Robot Teams: A Framework for Understanding Swift Trust

In this paper, we describe how swift trust in human-robot (HR) teams is developed through the incorporation of surface-level cues and imported information. Surface-level cues are physical characteristics whereas imported information serves to shape preconceived notions about the robot itself. A multidimensional continuum is used to explore the effects of varying surface-level cues and imported information on the type of swift trust that may form as a result: high, medium, and low swift trust. Similar surface cues and positive information suggest higher swift trust development, whereas, negative imported information and differing surface cues evoke low swift trust. Surface cues and imported information that are incongruous leads to medium swift trust formation. This paper offers insights into the development of swift trust depending on the specific traits relevant to both the human and robot team members.