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.

Technologies in machine learning and artificial intelligence have come a long way in decision making and system automation, but still faces difficult challenges in semi-automation and human-in-the-loop frameworks. This work presents a probabilistic policy blending approach for shared control between a human operator and an intelligent agent. The proposed approach assumes that the agent can control a system and the human operator needs to communicate the system’s intended goal. A comparative study is presented between different arbitration functions that are used to blend the human and agent’s actions. The proposed approach can achieve a variable level of assistance to the human operator successfully within discrete action space using the Lunar Lander game environment developed by OpenAI. Furthermore, human physiological data have been analyzed while the human interacts with the system and the agent using different arbitration functions. A correlation between the physiological data, arbitration level, and task performance was observed.

Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely ‘valence’ (The pleasantness of the emotion being displayed) and ‘arousal’ (The intensity of the emotion being displayed). Existing methods to estimate arousal and valence rely on learning from data sets, where an expert annotator labels every image frame. Access to an expert annotator is not always possible, and the annotation can also be tedious. Hence it is more practical to obtain self-reported arousal and valence values directly from the human in a real-time Human-Robot collaborative setting. Hence this paper provides an emotion data set (HRI-AVC) obtained while conducting a human-robot interaction (HRI) task. The self-reported pair of labels in this data set is associated with a set of image frames. This paper also proposes a spatial and temporal attention-based network to estimate arousal and valence from this set of image frames. The results show that an attention-based network can estimate valence and arousal on the HRI-AVC data set even when Arousal and Valence values are unavailable per frame.

Mistakes in high stress and critical multitasking environments, such as piloting an airplane and the NASA control room, can lead to catastrophic failures. The human's internal state (e.g., workload) may be used to facilitate a robot teammate's adaptations, such that the robot can interact with the human without negatively impacting overall team performance. Human performance has a direct correlation with workload states; thus, the human's internal workload state may be leveraged to adapt a robot's interactions with the human in order to improve team performance. A reinforcement learning-based paradigm that incorporates human workload states to determine appropriate robot adaptations is presented. Preliminary results using the proposed approach in a supervisory-based NASA MATB-II environment are presented.

SUMMARY: We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data. AVAILABILITY: http://rodeo.med.harvard.edu/tools/roundup.