Efficient human-agent collaboration requires understanding each other’s capabilities and establishing appropriate reliance. My thesis focuses on optimizing performance in mixed-initiative settings, where humans and agents dynamically contribute to decisions and actions. I first explore key factors shaping human reliance on decision-support agents, then examine how agents can model this reliance to initiate actions. My proposed work aims to enable agents to jointly provide decision and action support in multi-objective tasks, using bi-directional communication to enhance collaboration.

This article investigates people's trust and dependence on robotic decision support systems (DSSs), which provide cognitive assistance through suggestions. Robotic DSSs may not always offer optimal suggestions, requiring people to rely carefully to maximize performance. We analyze user reliance on suboptimal robots for solving instantaneous and sequential decision-making tasks with a math and card game, respectively. In instantaneous tasks, we find that the users' perceived anthropomorphism$(p < . 001$) and the robot's behavior after a decision support failure ($p < . 001$) significantly impact user trust. In a sequential task where the effectiveness of the human–robot team is not revealed until after several decisions, we find that introducing a user-initiated decision proposal before the robot reveals its recommendation can mitigate overreliance ($p < . 05$) and users' task expertise is critical in determining appropriate dependence on the robot's suggestions ($p < . 01$). Combined, these studies are synergistic and the first to jointly examine the influence of various factors on user trust and dependence, offering guidance for designing robotic DSSs to maximize human–robot task performance.

Advances in robot teleoperation have enabled groundbreaking innovations in many fields, such as space exploration, healthcare, and disaster relief. The human operator's performance plays a key role in the success of any teleoperation task, with prior evidence suggesting that operator stress and workload can impact task performance. As robot teleoperation is currently deployed in safety-critical domains, it is essential to analyze how different stress and workload levels impact the operator. We are unaware of any prior work investigating how both stress and workload impact teleoperation performance. We conducted a novel study ($n=24$) to jointly manipulate users' stress and workload and analyze the user's performance through objective and subjective measures. Our results indicate that, as stress increased, over 70% of our participants performed better up to a moderate level of stress; yet, the majority of participants performed worse as the workload increased. Importantly, our experimental design elucidated that stress and workload have related yet distinct impacts on task performance, with workload mediating the effects of distress on performance ($p< .05$).

With an aging population and a growing shortage of caregivers, the need for in-home robots is increasing. However, it is intractable for robots to have all functionalities pre-programmed prior to deployment. Instead, it is more realistic for robots to engage in supplemental, on-site learning about the user's needs and preferences. Such learning may occur in the presence of or involve the user. We investigate the impacts on end-users of in situ robot learning through a series of human-subjects experiments. We examine how different learning methods influence both in-person and remote participants' perceptions of the robot. While we find that the degree of user involvement in the robot's learning method impacts perceived anthropomorphism (p=.001), we find that it is the participants' perceived success of the robot that impacts the participants' trust in (p<.001) and perceived usability of the robot (p<.001) rather than the robot's learning method. Therefore, when presenting robot learning, the performance of the learning method appears more important than the degree of user involvement in the learning. Furthermore, we find that the physical presence of the robot impacts perceived safety (p<.001), trust (p<.001), and usability (p<.014). Thus, for tabletop manipulation tasks, researchers should consider the impact of physical presence on experiment participants.

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutual Information (GrAMMI) for modeling the behavior of an adversarial opponent agent. GrAMMI is a novel graph neural network (GNN) based approach that uses mutual information maximization as an auxiliary objective to predict the current and future states of an adversarial opponent with partial observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion domains inspired by real-world scenarios, where a team of heterogeneous agents is tasked with tracking and interdicting a single adversarial agent, and the adversarial agent must evade detection while achieving its own objectives. With the mutual information formulation, GrAMMI outperforms all baselines in both domains and achieves 31.68% higher log-likelihood on average for future adversarial state predictions across both domains.

As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices in HRI research. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, many HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of five years of the International Conference on Human-Robot Interaction (HRIc) (2016 through 2020) and report on incorrect statistical practices and design of Likert scales [ 1 , 2 , 3 , 5 , 7 ]. During these years, only 4 of the 144 papers applied proper statistical testing to correctly designed Likert scales. We additionally conduct a survey of best practices across several venues and provide a comparative analysis to determine how Likert practices differ across the field of Human-robot Interaction. We find that a venue’s impact score negatively correlates with number of Likert-related errors and acceptance rate, and total number of papers accepted per venue positively correlates with the number of errors. We also find statistically significant differences between venues for the frequency of misnomer and design errors. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Based on our findings, we provide guidelines and a tutorial for researchers for developing and analyzing Likert scales and associated data. We also detail a list of recommendations to improve the accuracy of conclusions drawn from Likert data.

As autonomous vehicles (AVs) become ubiquitous, users' trust will be critical for the successful adoption of such systems. Prior works have shown that the driving styles of AVs can impact how users trust and rely on such systems. However, users' preferred driving style may vary with changes in trust or road conditions, experience, and personal driving preferences. We explore methods to adapt the driving style of an AV to match the preferred driving style of users to improve their trust in the vehicle. We conducted a pilot study ($n=16$) on a simulated urban environment, where the users experience various static and adaptive driving styles for different pedestrian and traffic-related scenarios. Our results indicate that users best trust AVs that closely match their preferences ($p< 0.05$). We believe that exploring the effects of AV driving style on users' trust and workload will provide necessary steps towards developing human-aware automated systems.

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.

This paper examines how people's trust and dependence on robot teammates providing decision support varies as a function of different attributes of the robot, such as perceived anthropomorphism, type of support provided by the robot, and its physical presence. We conduct a mixed-design user study with multiple robots to investigate trust, inappropriate reliance, and compliance measures in the context of a time-constrained game. We also examine how the effect of human accountability addresses errors due to over-compliance in the context of human robot interaction (HRI). This study is novel as it involves examining multiple attributes at once, thus enabling us to perform multi-way comparisons between different attributes on trust and compliance with the agent. Results from the 4x4x2x2 study show that behavior and anthropomorphism of the agent are the most significant factors in predicting the trust and compliance with the robot. Furthermore, adding a coalition-building preface, where the agent provides context to why it might make errors while giving advice, leads to an increase in trust for specific behaviors of the agent.