Frontiers in Robotics and AI

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Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different “transparent” controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation.

Experiments on physical continuum robot are the gold standard for evaluations. Currently, as no commercial continuum robot platform is available, a large variety of early-stage prototypes exists. These prototypes are developed by individual research groups and are often used for a single publication. Thus, a significant amount of time is devoted to creating proprietary hardware and software hindering the development of a common platform, and shifting away scarce time and efforts from the main research challenges. We address this problem by proposing an open-source actuation module, which can be used to build different types of continuum robots. It consists of a high-torque brushless electric motor, a high resolution optical encoder, and a low-gear-ratio transmission. For this article, we create three different types of continuum robots. In addition, we illustrate, for the first time, that continuum robots built with our actuation module can proprioceptively detect external forces. Consequently, our approach opens untapped and under-investigated research directions related to the dynamics and advanced control of continuum robots, where sensing the generalized flow and effort is mandatory. Besides that, we democratize continuum robots research by providing open-source software and hardware with our initiative called the Open Continuum Robotics Project, to increase the accessibility and reproducibility of advanced methods.

Agriculture 4.0 presents several challenges for the automation of various operations, including the fundamental task of harvesting. One of the crucial aspects in the automatic harvesting of high value crops is the grip and detachment of delicate fruits without spoiling them or interfering with the environment. Soft robotic systems, particularly soft grippers, offer a promising solution for this problem, as they can operate in unstructured environments, manipulate objects delicately, and interact safely with humans. In this context, this article presents a soft gripper design for harvesting as well as for pick-and-place operations of small and medium-sized fruits. The gripper is fabricated using the 3D printing technology with a flexible thermoplastic elastomer filament. This approach enables the production of an economical, compact, easily replicable, and interchangeable gripper by utilizing soft robotics principles, such as flexible structures and pneumatic actuation.

Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity.

This paper presents and discusses the development and deployment of a tour guide robot as part of the 5 g-TOURS EU research project, aimed at developing applications enabled by 5G technology in different use cases. The objective is the development of an autonomous robotic application where intelligence is off-loaded to a remote machine via 5G network, so as to lift most of the computational load from the robot itself. The application uses components that have been widely studied in robotics, (i.e., localization, mapping, planning, interaction). However, the characteristics of the network and interactions with visitors in the wild introduce specific problems which must be taken into account. The paper discusses in detail such problems, summarizing the main results achieved both from the methodological and the experimental standpoint, and is completed by the description of the general functional architecture of the whole system, including navigation and operational services. The software implementation is also publicly available.

Introduction: The modern worldwide trend toward sedentary behavior comes with significant health risks. An accompanying wave of health technologies has tried to encourage physical activity, but these approaches often yield limited use and retention. Due to their unique ability to serve as both a health-promoting technology and a social peer, we propose robots as a game-changing solution for encouraging physical activity.

Methods: This article analyzes the eight exergames we previously created for the Rethink Baxter Research Robot in terms of four key components that are grounded in the video-game literature: repetition, pattern matching, music, and social design. We use these four game facets to assess gameplay data from 40 adult users who each experienced the games in balanced random order.

Results: In agreement with prior research, our results show that relevant musical cultural references, recognizable social analogues, and gameplay clarity are good strategies for taking an otherwise highly repetitive physical activity and making it engaging and popular among users.

Discussion: Others who study socially assistive robots and rehabilitation robotics can benefit from this work by considering the presented design attributes to generate future hypotheses and by using our eight open-source games to pursue follow-up work on social-physical exercise with robots.

Human-robot cooperation (HRC) is becoming increasingly relevant with the surge in collaborative robots (cobots) for industrial applications. Examples of humans and robots cooperating actively on the same workpiece can be found in research labs around the world, but industrial applications are still mostly limited to robots and humans taking turns. In this paper, we use a cooperative lifting task (co-lift) as a case study to explore how well this task can be learned within a limited time, and how background factors of users may impact learning. The experimental study included 32 healthy adults from 20 to 54 years who performed a co-lift with a collaborative robot. The physical setup is designed as a gamified user training system as research has validated that gamification is an effective methodology for user training. Human motions and gestures were measured using Inertial Measurement Unit (IMU) sensors and used to interact with the robot across three role distributions: human as the leader, robot as the leader, and shared leadership. We find that regardless of age, gender, job category, gaming background, and familiarity with robots, the learning curve of all users showed a satisfactory progression and that all users could achieve successful cooperation with the robot on the co-lift task after seven or fewer trials. The data indicates that some of the background factors of the users such as occupation, past gaming habits, etc., may affect learning outcomes, which will be explored further in future experiments. Overall, the results indicate that the potential of the adoption of HRC in the industry is promising for a diverse set of users after a relatively short training process.

Inflatable fabric beams (IFBs) integrating pleat folds can generate complex motion by modifying the pleat characteristics (e.g., dimensions, orientations). However, the capability of the IFB to return to the folded configuration relies upon the elasticity of the fabrics, requiring additional pressure inputs or complementary mechanisms. Using soft compliant elements (SCEs) assembled onto pleat folds is an appealing approach to improving the IFB elasticity and providing a range of spatial configurations when pressurized. This study introduces an actuator comprising an IFB with pleat folds and SCEs. By methodologically assembling the SCEs onto the pleat folds, we constrain the IFB unfolding to achieve out-of-plane motion at 5 kPa. Besides, the proposed actuator can generate angular displacement by regulating the input pressure (> 5 kPa). A matrix-based representation and model are proposed to analyze the actuator motion. We experimentally study the actuator’s angular displacement by modifying SCE shapes, fold dimensions, and assembly distances of SCEs. Moreover, we analyze the effects of incorporating two SCEs onto a pleat fold. Our results show that the actuator motion can be tuned by integrating SCEs with different stiffness and varying the pleat fold dimensions. In addition, we demonstrate that the integration of two SCEs onto the pleat fold permits the actuator to return to its folded configuration when depressurized. In order to demonstrate the versatility of the proposed actuator, we devise and conduct experiments showcasing the implementation of a planar serial manipulator and a soft gripper with two grasping modalities.

In the development of dialogue systems for android robots, the goal is to achieve human-like communication. However, subtle differences between android robots and humans are noticeable, leading even human-like android robots to be perceived differently. Understanding how humans accept android robots and optimizing their behavior is crucial. Generally, human customers have various expectations and anxieties when interacting with a robotic salesclerk instead of a human. Asymmetric communication arises when android robots treat customers like humans while customers treat robots as machines. Focusing on human-robot interaction in a tourist guide scenario, In this paper, we propose an asymmetric communication strategy that does not use estimation technology for preference information, but instead performs changing the agent’s character in order to pretend to tailor to the customer. In line with this, we prepared an experimental method to evaluate asymmetric communication strategies, using video clips to simulate dialogues. Participants completed questionnaires without prior knowledge of whether the salesclerk was human-like or robotic. The method allowed us to assess how participants treated the salesclerk and the effectiveness of the asymmetric communication strategy. Additionally, during our demonstration in a dialogue robot competition, 29 visitors had a positive impression of the android robot’s asymmetric communication strategy and reported a high level of satisfaction with the dialogue.

Introduction: In the current landscape marked by swift digital transformations and global disruptions, comprehending the intersection of digitalization and sustainable business practices is imperative. This study focuses on the food industries of China and Pakistan, aiming to explore the influence of digitalization on cleaner production.

Methods: Employing a cross-sectional design, data were gathered through online surveys involving a diverse group of employees. Special attention was given to the emergent phenomenon of technostress and its subsequent implications for individuals in the workplace.

Results: The findings of the study demonstrate a significant impact of digitalization on both resource mobilization and interaction quality within the surveyed food industries. Notably, technostress emerged as a mediating factor, shedding light on the psychological challenges associated with digital transitions. The study further reveals the moderating role of the COVID-19 pandemic, altering the dynamics among the variables under investigation.

Discussion: From a theoretical perspective, this research contributes to the cleaner production literature by bridging it with the human-centric nuances of technological adaptation. On a practical level, the study emphasizes the importance of aligning digital strategies with resource mobilization to achieve sustainable outcomes. For the food industry and potentially beyond, the research offers a roadmap for integrating digital tools into operations, ensuring efficiency, and promoting cleaner production.

A basic assumption in most approaches to simultaneous localization and mapping (SLAM) is the static nature of the environment. In recent years, some research has been devoted to the field of SLAM in dynamic environments. However, most of the studies conducted in this field have implemented SLAM by removing and filtering the moving landmarks. Moreover, the use of several robots in large, complex, and dynamic environments can significantly improve performance on the localization and mapping task, which has attracted many researchers to this problem more recently. In multi-robot SLAM, the robots can cooperate in a decentralized manner without the need for a central processing center to obtain their positions and a more precise map of the environment. In this article, a new decentralized approach is presented for multi-robot SLAM problems in dynamic environments with unknown initial correspondence. The proposed method applies a modified Fast-SLAM method, which implements SLAM in a decentralized manner by considering moving landmarks in the environment. Due to the unknown initial correspondence of the robots, a geographical approach is embedded in the proposed algorithm to align and merge their maps. Data association is also embedded in the algorithm; this is performed using the measurement predictions in the SLAM process of each robot. Finally, simulation results are provided to demonstrate the performance of the proposed method.

Smart haptic gloves are a new technology emerging in Virtual Reality (VR) with a promise to enhance sensory feedback in VR. This paper presents one of the first attempts to explore its application to surgical training for neurosurgery trainees using VR-based surgery simulators. We develop and evaluate a surgical simulator for External Ventricular Drain Placement (EVD), a common procedure in the field of neurosurgery. Haptic gloves are used in combination with a VR environment to augment the experience of burr hole placement, and flexible catheter manipulation. The simulator was integrated into the training curriculum at the 2022 Canadian Neurosurgery Rookie Bootcamp. Thirty neurosurgery residents used the simulator where objective performance metrics and subjective experience scores were acquired. We provide the details of the simulator development, as well as the user study results and draw conclusions on the benefits added by the haptic gloves and future directions.

Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge.

Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos.

Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans.

Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.

Musculoskeletal models provide an approach towards simulating the ability of the human body in a variety of human-robot applications. A promising use for musculoskeletal models is to model the physical capabilities of the human body, for example, estimating the strength at the hand. Several methods of modelling and representing human strength with musculoskeletal models have been used in ergonomic analysis, human-robot interaction and robotic assistance. However, it is currently unclear which methods best suit modelling and representing limb strength. This paper compares existing methods for calculating and representing the strength of the upper limb using musculoskeletal models. It then details the differences and relative advantages of the existing methods, enabling the discussion on the appropriateness of each method for particular applications.

Introduction: There has been a surge in the use of social robots for providing information, persuasion, and entertainment in noisy public spaces in recent years. Considering the well-documented negative effect of noise on human cognition, masking sounds have been introduced. Masking sounds work, in principle, by making the intrusive background speeches less intelligible, and hence, less distracting. However, this reduced distraction comes with the cost of increasing annoyance and reduced cognitive performance in the users of masking sounds.

Methods: In a previous study, it was shown that reducing the fundamental frequency of the speech-shaped noise as a masking sound significantly contributes to its being less annoying and more efficient. In this study, the effectiveness of the proposed masking sound was tested on the performance of subjects listening to a lecture given by a social robot in a noisy cocktail party environment.

Results: The results indicate that the presence of the masking sound significantly increased speech comprehension, perceived understandability, acoustic satisfaction, and sound privacy of the individuals listening to the robot in an adverse listening condition.

Discussion: To the knowledge of the authors, no previous work has investigated the application of sound masking technology in human-robot interaction designs. The future directions of this trend are discussed.

In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator’s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.

Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ.

Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances.

Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.

Introduction: Human–robot teams are being called upon to accomplish increasingly complex tasks. During execution, the robot may operate at different levels of autonomy (LOAs), ranging from full robotic autonomy to full human control. For any number of reasons, such as changes in the robot’s surroundings due to the complexities of operating in dynamic and uncertain environments, degradation and damage to the robot platform, or changes in tasking, adjusting the LOA during operations may be necessary to achieve desired mission outcomes. Thus, a critical challenge is understanding when and how the autonomy should be adjusted.

Methods: We frame this problem with respect to the robot’s capabilities and limitations, known as robot competency. With this framing, a robot could be granted a level of autonomy in line with its ability to operate with a high degree of competence. First, we propose a Model Quality Assessment metric, which indicates how (un)expected an autonomous robot’s observations are compared to its model predictions. Next, we present an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm that uses changes in the Model Quality Assessment above a threshold to selectively execute and report a high-level assessment of the robot’s competency. We validated the Model Quality Assessment metric and the ET-GOA algorithm in both simulated and live robot navigation scenarios.

Results: Our experiments found that the Model Quality Assessment was able to respond to unexpected observations. Additionally, our validation of the full ET-GOA algorithm explored how the computational cost and accuracy of the algorithm was impacted across several Model Quality triggering thresholds and with differing amounts of state perturbations.

Discussion: Our experimental results combined with a human-in-the-loop demonstration show that Event-Triggered Generalized Outcome Assessment algorithm can facilitate informed autonomy-adjustment decisions based on a robot’s task competency.

Soft pneumatic artificial muscles are a well actuation scheme in soft robotics due to its key features for robotic machines being safe, lightweight, and conformable. In this work, we present a versatile vacuum-powered artificial muscle (VPAM) with manually tunable output motion. We developed an artificial muscle that consists of a stack of air chambers that can use replaceable external reinforcements. Different modes of operation are achieved by assembling different reinforcements that constrain the output motion of the actuator during actuation. We designed replaceable external reinforcements to produce single motions such as twisting, bending, shearing and rotary. We then conducted a deformation and lifting force characterization for these motions. We demonstrated sophisticated motions and reusability of the artificial muscle in two soft machines with different modes of locomotion. Our results show that our VPAM is reusable and versatile producing a variety and sophisticated output motions if needed. This key feature specially benefits unpredicted workspaces that require a soft actuator that can be adjusted for other tasks. Our scheme has the potential to offer new strategies for locomotion in machines for underwater or terrestrial operation, and wearable devices with different modes of operation.

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