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The integration of human–robot teams is increasingly essential in dynamic task environments, particularly in sectors like warehouse management, assembly lines, search and rescue operations, material handling, and autonomous driving. This trend leverages the complementary strengths of humans and robots to enhance efficiency and tackle complex objectives. However, significant challenges arise due to differences in task execution, communication modes, empathy, mental model understanding, and adaptability between humans and robots. This survey article examines the complexities of human–robot collaboration (HRC), focusing on the “3Cs” of teamwork: collaboration, communication, and cognition. It introduces a novel 3Cs rating system to evaluate HRC systems, offering a comprehensive analysis of current research trends and identifying key challenges. The findings highlight a prevalent lack of robot adaptation based on human states and performance, underscoring the need for improved communication metrics and consistent definitions of collaborative frameworks. Key contributions include the development of the 3Cs rating system, an in-depth analysis of HRC research trends, and the identification of critical areas requiring further investigation to realize the full potential of human–robot teams. This article aims to guide future research and development, promoting more effective human–robot collaborations. 

In this study, we develop an experimental environment for studying human spatial awareness and memory as well as a neuro-computational connectionist model based on hyperdimensional computing, useful for modeling participants’ ability to store and recall stimuli. A qualitative comparison between the proposed model’s confidence, in which it recalls past stimuli, and a participant’s performance in recalling the same past stimuli, was conducted. The results demonstrate that the proposed memory model shows promise for modeling participant performance fall-off, offering a potentially useful tool for neuroergonomics research. 

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network’s utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method’s average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications. 

This research investigated human performance in response to task demands that may be used to convey information about the human to an artificial agent. We performed an experiment with a dynamic time-sharing task to investigate participants development of temporal awareness of the task event unfolding in time. Temporal awareness as an extension, or a special case, of situation awareness, may provide for useful measures of covert mental models applicable to numerous tasks and for input to human-aware AI agents. Temporal awareness measures may be used to classify human performance into the control modes in the contextual control model (COCOM): scrambled, opportunistic, tactical, and strategic. Twenty-one participants participated in a within subjects experiment with an abstract task of resetting four independent timers within their respective windows of opportunity. The results show that temporal measures of task performance are sensitive to changes in task disruptions and difficulty and therefore have promise for human-aware AI.