The uncertainties arising from the imperfection of the shared understanding during human-robot collaboration (HRC) is a critical challenge in developing real-world robots, which has attracted attention in various fields, especially in the manufacturing sector. Many research efforts have explored several key components and elements of HRC to reduce uncertainties. However, these efforts are mostly isolated from each other and few attempts have been made to develop generic frameworks to combine them for better HRC frameworks. This article contributes to this issue by reviewing the components of HRC research and developing a generic framework of purposeful communication to better arrive at a common view of uncertainties and collaboratively deal with those required by the tasks at hand holistically. The aspects of HRC components that can affect the shared understanding of humans and robots include the type of collaborative task, communication modalities, and decision-making of robots. After examining these aspects, we re-positioned the central problem to the cause of these uncertainties and proposed a new categorization of the available HRC scenarios considering communication channels, because communication strategies should be the main focus for reducing these uncertainties. This categorization will help to design better HRC frameworks that will lead to improving shared understanding and task performance, reducing uncertainties, and establishing trust, transparency, and safety. This paper proposes a comprehensive literature review and a new categorization of the currently used communication approaches by analyzing forty-nine selected articles from a wide range of articles in various databases.

Objective. Machine learning (ML) methods are used in different fields for classification and regression purposes with different applications. These methods are also used with various non-invasive brain signals, including Electroencephalography (EEG) signals to detect some patterns in the brain signals. ML methods are considered critical tools for EEG analysis since could overcome some of the limitations in the traditional methods of EEG analysis such as Event-related potentials (ERPs) analysis. The goal of this paper was to apply ML classification methods on ERP scalp distribution to investigate the performance of these methods in identifying numerical information carried in different finger-numeral configurations (FNCs). FNCs in their three forms of montring, counting, and non-canonical counting are used for communication, counting, and doing arithmetic across the world between children and even adults. Studies have shown the relationship between perceptual and semantic processing of FNCs, and neural differences in visually identifying different types of FNCs. Approach. A publicly available 32-channel EEG dataset recorded for 38 participants while they were shown a picture of an FNC (i.e., three categories and four numbers of 1,2,3, and 4) was used. EEG data were pre-processed and ERP scalp distribution of different FNCs was classified across time by six ML methods, including support vector machine, linear discriminant analysis, naïve Bayes, decision tree, K-nearest neighbor, and neural network. The classification was conducted in two conditions: classifying all FNCs together (i.e., 12 classes) and classifying FNCs of each category separately (i.e., 4 classes). Results. The support vector machine had the highest classification accuracy for both conditions. For classifying all FNCs together, the K-nearest neighbor was the next in line; however, the neural network could retrieve numerical information from the FNCs for category-specific classification. Significance. The significance of this study is in exploring the application of multiple ML methods in recognizing numerical information contained in ERP scalp distribution of different finger-numeral configurations.

In this study, we used multivariate decoding methods to study processing differences between canonical (montring and count) and noncanonical finger numeral configurations (FNCs). While previous research investigated these processing differences using behavioral and event-related potentials (ERP) methods, conventional univariate ERP analyses focus on specific time intervals and electrode sites and fail to capture broader scalp distribution and EEG frequency patterns. To address this issue a supervised learning classifier—support vector machines (SVM)—was used to decode ERP scalp distributions and alpha-band power for montring, counting, and noncanonical FNCs (for integers 1 to 4). The SVM was used to test whether the numerical information presented in FNCs can be decoded from the EEG data. Differences in the magnitude and timing of accuracy rates were used to compare the three types of FNCs. Overall, the algorithm was able to predict numerical information presented in FNCs beyond the random chance level accuracy, with higher rates for ERP scalp distributions than alpha-power. Montring had lower peak accuracy compared to counting and noncanonical configurations, likely due to automaticity in processing montring configurations leading to less distinct scalp distributions for the four numerical magnitudes (1 to 4). Paralleling the response time data, the peak decoding accuracy time for montring was earlier for montring (472 ms), compared to counting (577 ms) and noncanonical FNCs (604 ms). The results provide support for montring configurations being processed automatically, somewhat similar to number symbols, and provide additional insights for processing differences across different forms of FNCs. This study also highlights the strengths of decoding methods in EEG/ERP research on numerical cognition.

The goal of this article is to investigate the use of communication robots as tools to increase the safety of first responders (specifically, law enforcement officers (LEOs)) during social interactions or first-response situations. This article functions as the first stage of our investigation into communication robots for use by first responders. The LEOs in our study were provided with a mobile communication robot to determine if such robots could be of use in the public domain for the benefit of both civilians and LEOs. As part of our study, LEOs participated in training sessions where they worked with nonweaponized teleoperated communication robots and completed pretest-posttest surveys. The surveys captured LEOs’ perceptions and attitudes towards the robot and examined the impact of robot design features on LEOs’ acceptance of the communication robot. This article discusses the significance and potential use of communication robots as tools in the future of law enforcement. Our future works include conducting additional studies involving affected stakeholders (e.g., citizen communities) and assessing/mitigating any potential negative impact of communication robots.

This study explores the perspectives of law enforcement officers (LEOs) on the implementation of communication robots in police work. Through two approaches - open-ended questionnaires (N = 37) and focus group discussions (N = 14) - the research aims to gather LEOs' opinions and attitudes on the potential future use of these robots. The findings reveal a general skepticism among LEOs, who express low trust in communication robots. Despite this, a majority acknowledge that robots could enhance communication effectiveness in policing. The study highlights the need for increased public education and enhanced performance accuracy of robots to address LEOs' concerns and foster greater acceptance of this technology in law enforcement.

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire response, their deployment inside buildings remains limited due to constraints in system mass, cost, and operational complexity. This paper presents the design and preliminary validation of an attritable micro-UAV as a proof-of-concept platform for indoor search support and post-fire inspection and assessment. The platform emphasizes portability, durability, and multi-sensor integration, enabling deployment by minimally trained personnel. System requirements were derived in collaboration with the Southfield Fire Department. The finalized design achieved a total mass of 247.34 g at a cost of $2969. Experimental evaluation demonstrated reliable sensing and communication performance at the subsystem level and confirmed structural robustness through drop tests from heights up to 3 m. Endurance testing yielded a maximum flight time of 28 min, slightly below the targeted 30 min requirement. While full task-level validation in operational firefighting scenarios has not been conducted, the proposed platform establishes a foundation for future development, including system-level validation, post-fire structural assessment, and enhanced visualization interfaces for improved situational awareness in emergency response operations.