After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1$\times 3$between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects' SA over the other conditions.

Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex operational environments. Despite its importance, trust is rarely incorporated into task allocation and reallocation algorithms for MH-MR collaboration. While prior research in single-human, single-robot interactions has shown that integrating trust significantly enhances both performance outcomes and user experience, its role in MH-MR task allocation remains underexplored. In this paper, we introduce the Expectation Confirmation Trust (ECT) Model, a novel framework for modeling trust dynamics in MH-MR teams. We evaluate the ECT model against five existing trust models and a no-trust baseline to assess its impact on task allocation outcomes across different team configurations (2H-2R, 5H-5R, and 10H-10R). Our results show that the ECT model improves task success rate, reduces mean completion time, and lowers task error rates. These findings highlight the complexities of trust-based task allocation in MH-MR teams. We discuss the implications of incorporating trust into task allocation algorithms and propose future research directions for adaptive trust mechanisms that balance efficiency and performance in dynamic, multi-agent environments.

This paper presentsMOCAS, a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To buildMOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self-Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin (SAM), which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.

The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams.

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offer recommendations for future research on security robots.

This paper explores how the personality traits of robot operators can influence their task performance during remote control of robots. It is essential to explore the impact of personal dispositions on information processing, both directly and indirectly, when working with robots on specific tasks. To investigate this relationship, we utilize the open-access multi-modal dataset MOCAS to examine the robot operator's personality traits, affect, cognitive load, and task performance. Our objective is to confirm if personality traits have a total effect, including both direct and indirect effects, that could significantly impact the performance levels of operators. Specifically, we examine the relationship between personality traits such as extroversion, conscientiousness, and agreeableness, and task performance. We conduct a correlation analysis between cognitive load, self-ratings of workload and affect, and quantified individual personality traits along with their experimental scores. The findings show that personality traits do not have a total effect on task performance. A supplementary video can be accessed at: https://youtu.be/h3XUtVn7nzg.

Autonomous shape and structure formation is an important problem in the domain of large-scale multi-agent systems. In this paper, we propose a 3D structure representation method and a distributed structure formation strategy where settled agents guide free moving agents to a prescribed location to settle in the structure. Agents at the structure formation frontier looking for neighbors to settle act as beacons, generating a surface gradient throughout the formed structure propagated by settled agents. Free-moving agents follow the surface gradient along the formed structure surface to the formation frontier, where they eventually reach the closest beacon and settle to continue the structure formation following a local bidding process. Agent behavior is governed by a finite state machine implementation, along with potential field-based motion control laws. We also discuss appropriate rules for recovering from stagnation points. Simulation experiments are presented to show planar and 3D structure formations with continuous and discontinuous boundary/surfaces, which validate the proposed strategy, followed by a scalability analysis.

While research on human-robot interaction is ongoing as robots become more readily available and easier to use, the study of interactions between a human and a team of multiple robots represents a relatively new field of research. In particular, how multi-robots could be used for everyday users and how the characteristics of multi-robots would affect human perception and cognition has not been explored. In this paper, we specifically focus on physical affordances generated by the movements of multi-robots, and investigate the effects of deictic movements of multi-robots on information retrieval by conducting a delayed free recall task. We conclude with further discussion of how the movements of the multi-robot reshape the way of people perceiving information and what should be considered to design a multi-robot-based display.

In this paper, we propose an autonomous exploration and a tapping mechanism-based material mapping system for a mobile robot in unknown environments. The goal of the proposed system is to integrate simultaneous localization and mapping (SLAM) modules and sound-based material classification to enable a mobile robot to explore an unknown environment autonomously and at the same time identify the various objects and materials in the environment. This creates a material map that localizes the various materials in the environment which has potential applications for search and rescue scenarios. A tapping mechanism and tapping audio signal processing based on machine learning techniques are exploited for a robot to identify the objects and materials. We demonstrate the proposed system through experiments using a mobile robot platform installed with Velodyne LiDAR, a linear solenoid, and microphones in an exploration-like scenario with various materials. Experiment results demonstrate that the proposed system can create useful material maps in unknown environments.

This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using the Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data were collected using the latest commercial wearable sensors, behavioral data were collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data were stored in single ROSbag files rather than in conventional Comma-Separated Values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and the size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be a great resource for many researchers in the fields of affective computing, Human-Computer Interaction (HCI), and Human-Robot Interaction (HRI).

We present a new multi-robot system as a means of creating a visual communication cue that can add dynamic illustration to static figures or diagrams to enhance the power of delivery and improve an audience's attention. The proposed idea is that when a presenter/speaker writes something such as a shape or letter on a whiteboard table, multiple mobile robots trace the shape or letter while dynamically expressing it. The dynamic movement of multi-robots will further stimulate the cognitive perception of the audience with handwriting, positively affecting the comprehension of content. To do this, we apply image processing algorithms to extract feature points from a handwritten shape or letter while a task allocation algorithm deploys multi-robots on the feature points to highlight the shape or letter. We present preliminary experiment results that verify the proposed system with various characters and letters such as the English alphabet.