Abstract Data spaces are collaborative environments where data is exchanged across organizations with agreed-upon rules, and they are emerging as crucial infrastructures for secure and efficient data sharing in the digital economy. This paper provides a comprehensive analysis of various prominent European data space architectures, specifically IDS-RAM, Gaia-X, FIWARE, and IHAN, using a structured methodology to evaluate their characteristics, strengths, and limitations. In addition, recommendations for enhancing security protocols and privacy protection mechanisms are proposed, which are essential for building trust in data sharing environments. Finally, advancements in current frameworks are identified, along with a set of proposed extension requirements designed to enhance the capabilities of data spaces. These suggestions focus on the need for standardized governance, improved interoperability, and mechanisms to ensure data sovereignty and ethical use. By addressing these areas, the paper aims to support the development of more robust data-sharing infrastructures that can adapt to the evolving landscape of data management, with a strong focus on maintaining the highest standards of security and privacy protection.

Abstract Embodied virtual agents (EVAs) are beginning to be researched to improve human–computer interaction. As EVAs become increasingly integrated into various aspects of daily life, understanding how to optimize their design to foster trust and likability among users is paramount. Leveraging insights from social psychology, particularly the concept of homophily, this study investigates the impact of perceived personality traits on user perceptions of EVAs. Specifically, we explore whether aligning the personality traits of EVAs with those of users increases engagement and fosters positive interactions. Drawing on a sample of 382 participants recruited through Amazon Mechanical Turk, we assessed participants' personality traits using the Big Five Inventory—2S, while the perceived extroversion of the agent was manipulated through facial expressions and body posture. Our findings suggest that participants were able to accurately identify the perceived extroversion of the agent (p = .014), and significant results indicate a homophily effect on trust, with participants exhibiting greater trust in agents perceived as having a similar level of extroversion (p < .01). However, no significant effect on likability was detected, suggesting a more nuanced relationship between perceived personality traits and user preferences. These findings highlight the potential of leveraging homophily in designing more engaging EVAs and underscore the importance of considering user–agent compatibility in human–computer interaction.

Grippers act as the physical link between robotic manipulators and their surroundings, allowing robots to handle objects and perform a variety of tasks, such as the manipulation of grapes during harvesting. To improve the design of grippers, we propose an automated framework, which starts with an initial design that resembles human hands’ configuration during grape harvesting and optimizes it based on the characteristics of the grape variety to be harvested. In this direction, the proposed framework utilizes parametric models for both the grape and the gripper and integrates them into an automated grasp planning simulation procedure. All the components are combined in a custom graphical user interface (GUI), making the entire process more user-friendly. Results from our experimentation showed that the proposed framework has the potential to significantly improve the efficiency and effectiveness of gripper design, especially for applications involving the handling of objects with unique parameters and requirements.

Reliable and robust navigation of autonomous mobile robots in indoor environments faces significant challenges due to the absence of GPS, visual degradation, repetitive structures, illumination variations, and low texture. These factors adversely affect localization systems. Current robots often use a uniform navigation approach, regardless of the varying localization uncertainties within different indoor environments. In this paper, we propose a holistic, active vision-based path planning method that produces efficient trajectories, aiming to minimize localization error and enhance navigation performance. Specifically, we utilize a 3D model of an indoor environment to derive an Artificial Potential Field (APF) with its associated localizability scores that encapsulate both visual features’ richness and fiducial markers’ placement. APF is employed to direct a Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (KB-RRT) planner towards the calculation of optimal paths, prioritizing high localization areas. Subsequently, we use an online weight-adaptive MPC-based approach that, apart from robust path planning and obstacle avoidance, guides the robot towards areas with the most robust visual features in order to further refine the localization error. The proposed framework has been extensively tested in both simulation and real-world experiments with a mobile robot in a visually challenging indoor environment.

Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like. Our approach starts with a novel method that we propose for extracting free space area using RGB and LiDAR data, in combination with a rough topometric map for route planning. Local goals are extracted in the final drivable region, and the vehicle draws a local path in the free space that is approximately in line with the overall path via a lattice planner. Our method is evaluated both in the publicly available KITTI urban dataset and a custom-made dataset of a semi-structured environment. In both cases, the results highlight the potential of our approach for further advancements in autonomous navigation and the development of safer and more human-like behaviors in driverless vehicles compared to the existing trajectory prediction state-of-the-art methods that make use of a topometric map.

Automation of vineyards cultivation necessitates for mobile robots to retain accurate localization system. The paper introduces a stereo vision-based Graph-Simultaneous Localization and Mapping (Graph-SLAM) pipeline custom-tailored to the specificities of vineyard fields. Graph-SLAM is reinforced with a Loop Closure Detection (LCD) based on semantic segmentation of the vine trees. The Mask R-CNN network is applied to segment the trunk regions of images, on which unique visual features are extracted. These features are used to populate the bag of visual words (BoVW s) retained on the formulated graph. A nearest neighbor search is applied to each query trunk-image to associate each unique feature descriptor with the corresponding node in the graph using a voting procedure. We apply a probabilistic method to select the most suitable loop closing pair and, upon an LCD appearance, the 3D points of the trunks are employed to estimate the loop closure constraint to the graph. The traceable features on trunk segments drastically reduce the number of retained BoVWs, which in turn expedites significantly the loop closure and graph optimization, rendering our method suitable for large scale mapping in vineyards. The pipeline has been evaluated on several data sequences gathered from real vineyards, in different seasons, when the appearance of vine trees vary significantly, and exhibited robust mapping in long distances.

This work presents a comparative analysis of human-crafted and automated feature extraction approaches for the discrimination of hand-based activities among eating, drinking and smoking. In this scheme, accelerometer and gyroscope sensors were utilised to capture activity signals. For this reason, wearable devices that embed the aforementioned sensors were employed to collect activity data from 12 office workers. The two approaches that were developed for feature mapping were evaluated equally on the collected dataset. Both the proposed schemes achieved to classify the hand-based activities. However, based on the experimental process, this study shows that the human-crafted features that extracted valuable information from the time and frequency domain of the raw signal measurements outperformed the automated feature mapping that utilised deep learning advances. The relevant results prove that the human-crafted features can recognise hand-based activities with 0.9109 and, on the other hand, automated features with a 0.907 F1 weighted score over the dataset.

Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-based method enabling safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology relying on monocular vision and laser-based measurements has been applied and installed on-board a novel robotic pallet truck. Pallet detection and pose estimation are performed in two steps. Firstly, a deep neural network is used for the fast isolation of pallets' regions of interest and, secondly, model-based geometrical pattern matching on point cloud data is applied to extract the pallet pose. Robot alignment with candidate pallet is performed with a dedicated visual servoing controller. The developed method has been extensively evaluated both in simulated and real industrial environments with the pallet truck and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up.

Acoustics has received a great research interest for human activity detection in indoor and outdoor environments. Compared to vision-based approaches, microphones can achieve a high percentage of recognition accuracy in a variety of activities, while not being affected by lighting conditions. Furthermore, audio-based activity detection can be considered an unobtrusive method, as long as the data is not related to speech or other sensitive information and no data is sent on cloud. Selecting the appropriate audio features that can achieve a high recognition accuracy and generalize in multiple domestic environments is a challenging task. In this work, three of the most commonly used spectrogram representations are evaluated, based on their spectral magnitude and the spectral energies. Specifically, using multi-channel audio data, the Short-time Fourier Transform (STFT), the Mel and the Gammatone spectrograms are extracted and trained on a 2D Convolutional Neural Network (CNN). The F1-Scores of each feature representation are computed, while the McNemar tests and the Receiver Operating Characteristic (ROC) curves ensure the statistical independence between the magnitude and the energy representations. Extensive experimental results on a public database for detection of daily activities in a home environment, show that the overall highest recognition accuracy is achieved by the STFT magnitude representations.

As autonomous collaborative robots are more widely used in work environments alongside humans it is of great importance to facilitate the communication between people and robotic systems, in a way that promotes safety and productivity. To this end, we propose an Augmented Reality (AR) based system that allows workers in a human-robot collaborative environment to interact with a robot while also receiving information regarding the robot state and plans that relate to the human’s safety and trust, such as the intended movement of the robotic arm or the navigation plan of the mobile platform. To evaluate the effectiveness of the proposed system we conducted experiments with 13 participants, where two users had to work in the same workspace while being assisted by a mobile manipulator. We measured the task completion time as well as the robot idle time using our AR-based human-robot interaction system and compared them to a conventional setup without the use of augmented reality. Additional, subjective evaluations related to user satisfaction, system usability, perceived safety and trust showed that users assessed the system in a positive way and preferred AR visualization over more traditional interfaces.

The contemporary SLAM mapping systems assume a static environment and build a map that is then used for mobile robot navigation disregarding the dynamic changes in this environment. The paper at hand presents a novel solution for the problem of life-long mapping that continually updates a metric map represented as a 2D occupancy grid in large scale indoor environments with movable objects such as people, robots, objects etc. suitable for industrial applications. We formalize each cell's occupancy as a failure analysis problem and contribute temporal persistence modeling (TPM), an algorithm for probabilistic prediction of the time that a cell in an observed location is expected to be “occupied” or “empty” given sparse prior observations from a task specific mobile robot. Our work is evaluated in Gazebo simulation environment against the nominal occupancy of cells and the estimated obstacles persistence. We also show that robot navigation with life-long mapping demands less replans and leads to more efficient navigation in highly dynamic environments.

Abstract The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.

Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions.

Automatic subsurface mapping is essential in the construction services, as it is anticipated to become the main operational environment of the future robots to be realized in the respective domain. Towards this direction, the paper at hand, introduces for the first time herein, an integrated framework for subsurface mapping by exploiting a surface operating mobile robot with a Ground Penetrating Radar (GPR). The mobile robot tows the GPR antenna, which is mounted on a specifically designed trailer, and is utilized as the mean to cover the surface area, while at the same time the antenna scans the subsurface by emitting electromagnetic pulses. The gathered data are processed for the construction of a subsurface 3D map. Specifically, image processing techniques, that involve background segmentation, HOG [1] feature extraction, hypothesis verification and matching are applied on the 2D radargram (B-Scan) for the detection of the salient points that correspond to buried utilities. By employing the pulse propagation velocity into the subsurface and the soil utilities, the salient points are expressed in world coordinates and used for the composition of the 3D subsurface map. Our method has been evaluated on a real test site, accompanied by ground-truth annotation data of experts and revealed remarkable performance, exhibiting not only the feasibility of underground mapping but also the capacity to obtain exploitable results for underground robotic applications.

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot was developed as an integrated ensemble of advanced H/W and S/W components, realizing the robot skills of perception, cognition, safe navigation, grasping, manipulation, and human-robot communication, ample to operate in real, rather challenging domestic environments. The RAMCIP use-cases include proactive assistance provision to user's cooking, eating and medication activities, through discreet user monitoring and robot interventions by reminders and robotic manipulations., RAMCIP can bring the medicine, recognize fallen objects and electric appliance that has been forgotten turned on. It also recognizes the user walking in low-light conditions and turns on the light, as well as detects cases of emergency such as a fall. The robot provides also the user with cognitive training games and stimulates the user to contact with relatives through video-calls. Pilot trials of the RAMCIP robot have been performed in real homes of more than ten different users, in Barcelona, Spain; the video at hand exhibits the robot performing the target use cases.

For a proactive and user-centered robotic assistance and communication, an assistive robot must make decisions about the level of assistance to be provided. Therefore, the robot must be aware of the preferences and the capabilities of the elderly. At the same time, relying on a sensing setup which is totally embedded in the assistive robot would increase its usability. In the framework of the RAMCIP project, a novel skills evaluation methodology has been developed to make the robot aware of the user's perceptual, cognitive and motor skills. This paper presents such a methodology and its preliminary evaluation. Based on a task analysis of the activities for which the robot provides assistance, the user's skills are given a score which is updated at different time scales based on the source of information. Highly reliable information is gathered from caregivers at a low rate by means of a graphical interface hosted by the robot. This information refers to standard medical examinations. Based on the modules for motion tracking, object and activity recognition, specific actions of ADL are selected to update motor skills score at a higher rate, which is typically twice per day. The two sources of information are then fused in a Kalman filter. Preliminary results on the illustrative example of arm precision show that the robot's sensing and cognitive capabilities suffice to obtain a state-of-the-art evaluation of the arm precision skill.

This paper presents work conducted toward the biosignals-based automatic recognition of boredom, induced during video-game playing. For this purpose, common biosignal feature extraction methods were exploited and their capability to identify boredom was assessed. Moreover, for the first time, Legendre and Krawtchouk moments, as well as novel moment variations, were extracted as biosignal features and their potential toward automatic affect recognition was examined using the specific application scenario. The present analysis was conducted with ECG and GSR data collected from 19 different subjects, while boredom was naturally induced during the repetitive playing of a 3D video game. Conventional biosignal features as well as moment-based ones were found to be effective for the automatic recognition of boredom by achieving classification accuracies around 85 percent. Then, the joint use of moments and moment variations with conventional features was found to significantly improve classification accuracy by producing a maximum correct classification ratio of 94.17 percent.