Ann Majewicz Fey

The impact of object vibrations on the capabilities of robot grasping systems is very significant. The process of vibration generation from the method of grasping, even with the same gripping device, will be very different. Therefore, the need to analyze such processes in automated robotic cells creates a gap for further research to overcome vibration or use it for useful purposes.

Transcranial Doppler (TCD) ultrasound is a portable, non-ionizing imaging modality widely used for real-time monitoring of cerebral blood flow in applications such as vasospasm detection, emboli tracking, autoregulation assessment, and brain death confirmation. However, conventional TCD systems lack anatomical context, are highly operator-dependent, and exhibit inconsistent angle alignment, thereby limiting measurement accuracy and reproducibility. This feasibility study presents a tracked, freehand 3D Doppler ultrasound system that reconstructs volumetric blood flow maps using electromagnetic tracking and standard clinical ultrasound platforms. Four adult subjects were scanned through bilateral transtemporal acoustic windows, and 600–1000 Doppler frames were collected per subject following spatial calibration. Volumetric reconstructions were generated offline, and vessel diameters were measured from orthogonally reformatted axial views of the M1 and P1 segments. Measured values showed a strong correlation with Digital Subtraction Angiography (DSA)-based normative values (Pearson r = 0.99, p=0.0093), with standard deviations < 0.35mm across all segments. Expert neurologists confirmed anatomical consistency of the bilateral MCA, PCA, and ACA reconstructions across all subjects. These findings support the potential of tracked 3D Doppler ultrasound as a non-invasive, real-time imaging modality for intracranial vascular assessment. The ability to generate co-registered volumetric maps with anatomical context addresses key limitations of conventional TCD, including operator dependence and lack of spatial guidance. Retrospective visualization enables correction of insonation angle and vessel localization even in the presence of anatomical distortion. This framework also lays the groundwork for automated or semiautomated acquisition workflows, enabling standardized, reproducible imaging in neurocritical care and bedside cerebrovascular monitoring.

The integration of automated systems for the manipulation of deformable objects in manufacturing remains a time-consuming process. This challenge arises primarily from the absence of standardized grasping parameters applicable to the full range of manufactured products and materials. This paper presents a method for automating the study of the lifting parameters of deformable objects using a robotic manipulator with a pneumatic grasping system. A detailed description of the design, implementation, and evaluation of a custom pneumatic gripper integrated with a robot arm is provided. Through experiments utilizing various gripping surfaces and materials, the influence of surface patterns, material properties, and pneumatic pressures on lifting performance was investigated. The results demonstrate significant correlations between material type, surface design, and supply pressure in the context of gripping porous objects. The proposed method enables rapid characterization of the interaction between materials and pneumatic grasping systems. This approach facilitates the integration of pneumatic gripping systems into fully automated manufacturing lines handling deformable objects.

The convergence of robotics, artificial intelligence, and human-centered design is transforming healthcare and participation across diverse populations (1)(2)(3). Interactive robots, ranging from surgical robots to empathetic healthcare assistants, are increasingly being deployed to address critical challenges in healthcare delivery, aging, mental health, and rehabilitation (4)(5)(6)(7)(8). This Research Topic explores the multifaceted role of interactive robotics in enhancing care, promoting autonomy, and supporting participation, particularly in contexts of vulnerability and cognitive or physical decline.Healthcare systems around the world are facing mounting pressure due to demographic shifts, workforce shortages, and the rising demand for personalized, high-quality care (9). As populations age and chronic conditions become more prevalent, the urgency for scalable, adaptive, and emotionally intelligent technologies grows. Interactive robots offer a promising solution: they can automate routine tasks, provide companionship, support rehabilitation, and facilitate mental health screening while maintaining a human-centered approach (6-8).However, translating these innovations into everyday practice remains challenging. Ethical concerns, usability barriers, and cultural differences often hinder adoption (10,11), while the slow transfer of research prototypes into real-world applications further compounds the problem. Effective integration requires not only technical excellence but also alignment with clinical workflows, user expectations, and organizational realities (12).Recent advances show that interactive robotics can significantly enhance patient engagement, improve job satisfaction for healthcare professionals, and boost operational efficiency (13,14). Realizing this potential requires close collaboration between researchers and practitioners to bridge the gap between innovation and everyday clinical practice (15)(16)(17). This Research Topic responds to that need by presenting interdisciplinary contributions that demonstrate how interactive robots can be effectively designed, evaluated, and integrated into healthcare workflows. Despite these promising developments, a critical challenge remains: aligning technological capabilities with real-world requirements. The following section explores how contributions in this collection address this gap through participatory design, clinician-informed development, and longitudinal deployment.Contributions to the TopicAhmadi Majd et al. introduced a machine learning-based web application for screening Social Anxiety Disorder (SAD) using multimedia scenarios and emotion regulation strategies. Their tool achieved high accuracy and reliability, distinguishing between SAD and non-SAD individuals based on their use of suppression, avoidance, and reappraisal. The authors propose integrating this screening approach into socially-assistive robots to improve accessibility and engagement, particularly for individuals reluctant to seek traditional care.Naderer et al. presents a smart robotic walking aid designed to reduce fall risk in older adults through real-time balance monitoring and corrective actuation. The system combines a wearable inertial measurement unit, PID-controlled actuators, and an electromechanical braking mechanism to detect and respond to postural imbalances. Experimental validation demonstrated rapid stabilization and user safety, highlighting the potential of robotics to enhance mobility and independence in aging populations.Hofstede et al. explored the use of huggable integrated socially assistive robots (HI-SARs) in longterm care settings (eldercare, disability care, and rehabilitation). These robots combine emotional comfort, verbal interaction, and activity monitoring. Across three studies, HI-SARs were wellreceived, particularly to support clients with cognitive impairments such as dementia. The authors emphasize the importance of personalization, hygiene management, and proactive use of sensor data to enhance effectiveness.Gebellí and Ros present a comprehensive in-situ participatory design methodology for assistive robots in healthcare settings. Their 10-month study at an intermediate healthcare center culminated in a 2-month deployment of an autonomous patrolling and room monitoring robot to enhance patient safety. The iterative co-design process, involving healthcare staff and low-fidelity prototypes, revealed five distinct user personas and highlighted the importance of context-based requirements gathering, adaptive interaction design, and sustained engagement.Oliva et al. extend this theme to speech-language pathology, employing a four-week asynchronous remote community (ARC) approach with licensed clinicians. Their findings highlight the need for expressive, multimodal communication, customizable behaviors, and strong ethical safeguards. The study reinforces the value of clinician-centered design and positions socially assistive robots (SARs) as tools to augment (not replace) therapeutic relationships.Tang and Dou provide an economic perspective through a systematic review and meta-analysis of robotic surgery in older adults. While their results confirm the cost-effectiveness of robotic interventions when long-term outcomes and quality-adjusted life years (QALYs) are considered, they also identify high initial investment as a significant barrier to widespread adoption.Finally, Nguyen and Saito examine the implementation of nursing care robots in Japan, emphasizing the critical role of hands-on training, user experience, and collaboration between developers and educators. Their study reveals that although care robots can improve operational efficiency and reduce staff stress, concerns about usability, cost, and emotional burden persist.Together, these contributions underscore the transformative potential of interactive robots in healthcare and reveal critical priorities for future development. First, personalization and adaptability emerge as central themes: robots must be tailored or adaptable to individual needs, preferences, and cultural contexts. Features such as customizable voices, adaptive feedback, and scenario-based interaction enhance engagement and therapeutic effectiveness.Equally important is ethical and practical integration. Concerns around data privacy, emotional attachment, and task replacement demand responsible innovation frameworks and co-design approaches that actively involve caregivers, clinicians, and patients. It is generally agreed that robots should complement (not replace) human relationships, ensuring emotional safety and trust.Advances in multimodal interaction--combining speech, touch, motion, and sensor data--enable richer, context-aware experiences. At the same time, scalability and accessibility remain essential for extending care to underserved populations. Web-based tools and cost-effective robotic platforms can bridge gaps in rural communities and support individuals with social anxiety or mobility limitations.Finally, longitudinal evaluation is critical. Several studies in this collection highlight the need for extended deployments and iterative refinement to understand user behavior, system performance, and real-world impact. Sustained engagement and adaptive design processes will ensure that interactive robots evolve from promising prototypes into practical, widely adopted solutions.Interactive robots are moving beyond research labs into homes, clinics, and care facilities, offering new ways to support health, participation, and wellbeing. This Research Topic demonstrates how thoughtful design, rigorous evaluation, and interdisciplinary collaboration can unlock their full potential. The challenge ahead is not only technological but deeply human: ensuring these systems act as partners in care, enhancing dignity, autonomy, and quality of life across diverse populations.Achieving widespread adoption will require overcoming barriers related to cost, infrastructure, regulatory frameworks, and workforce readiness. Success depends on robust evidence of clinical impact, proactive change management, and strategies that align innovation with human-centered care. By addressing these factors, interactive robots can evolve from promising prototypes into trusted tools that transform healthcare delivery.

The imitation learning paradigm is a systematic approach for encoding intelligent behaviors into robotic systems. While a model representation of the ideal task behavior can be learned by processing a set of human demonstrations, learning a modeling representation that can generalize the desired behavior to perform well in dynamic environments with human collaborators is an open challenge. To address this problem, we encode intelligent robot behavior as a combination of a popular learned baseline control policy (Gaussian Mixture Model, GMM) with reactive control policies that activate based on triggers from online sensory information during task execution. Two contributions encapsulate the approach: an iterative algorithm to combine the learned and reactive policies and examples for mapping sensory information into desired robot reactive behaviors. The proposed approach was implemented on a bi-manual surgical robot and evaluated on how well the combined control policy balanced the behavioral constraints imposed during a collision avoidance and compliance tasks. Successful dynamic collision avoidance results and compliance responses that reduce environmental forces on the manipulator support the use of this paradigm for designing intelligent robot behaviors which can complement learned models to program complex robot behaviors that can balance task performance in scenarios with human collaborators.

Communication between a human and an intelligent robotic system is an essential component for facilitating effective collaboration. In this paper, we describe a preliminary methodology for creating a collaborative surgical assistant. The proposed framework utilizes Gaussian Mixture Modeling (GMM) of surgical skills. Our approach leverages the GMM to allow for a human to synchronously execute manipulation tasks with the robotic system. Key components of our approach are modified GMM regression techniques which provide the synchronized control policy for the surgical assistant (automatic) manipulator and communicates trajectory information in the form of augmented reality visual cues and haptic guidance proprioceptive feedback to the human operator during task execution. A semi-autonomous surgical knot tie experiment, where the human operator collaborates with an automatic manipulator, was conducted to validate the proof of concept. We show that the variability by the human user allows the team to complete the task and provide insights into future system improvements.

Effective grasping and manipulation of deformable objects remains a challenge for most manufacturing applications. However, advances in gripper technology are moving the automation of this process closer to reality. Despite their potential, our previously developed state-of-the-art gripper and other jet grippers have the disadvantage of vibrating the deformable object during grasping. This results in a reduction of frictional force between the gripper and the deformable object, thereby impairing the ability to effectively perform manipulation. In this paper, we successfully eliminated vibrations during the grasping and manipulation of textile objects by introducing a novel anti-vibration grid that redirects airflow away from the object. Additionally, a comprehensive parametric study led to a significant increase in the gripper’s force characteristics. Future work will focus on advancing dexterous manipulation and integrating the gripper with other actuators for improved performance and efficiency.

Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.

Force sensing on hands can provide an understanding of interaction forces during manipulation, with applications in different fields, including robotics and medicine. While several approaches to accomplish this have been proposed, they often require relatively complex and/or expensive fabrication techniques and materials. On the other hand, less complex and expensive approaches often suffer from poor accuracy of measurements. An example of this is provided by sensors built with Velostat, a polyethylene-carbon composite material that exhibits resistance changes when force is applied. This material is both cheap and easy to work with, but sensors made from Velostat have been shown to suffer from low accuracy, limiting its usefulness. This work explores the effect of stacking multiple layers of 0.1 mm Velostat sheets on accuracy, using no additional fabrication techniques or other material aside from electrode connections, with the rationale that this is both economical and can be accomplished easily. We evaluate measurement error for designs with different numbers of layers (1, 3, 4, 5, 10, 20, and 30) against a load cell, and also compare this with the error for a USD 10 commercial force sensing resistor designed for measurement of hand forces (FSR 402) in three evaluations (static, cyclic, and finger base interactions). Our results show that layered sensors outperform both the one-layer design and the commercial FSR sensor consistently under all conditions considered, with the best performing sensors reducing measurement errors by at least 27% and as much as 60% when compared against the one-layer design.

Observational gait analysis and categorical ratings are commonly used by clinicians to assess pathologies. The purpose of this study was to determine the capacity of novice observers to characterize the gait behavior underlying biomechanical performance objectives using stylistic labels. We hypothesized that visual characterization of physics-based musculoskeletal predictive simulations of walking would be sensitive to the biomechanical objective employed by individuals, as well as the visual perspective. We developed 75 full-body muscle-driven predictive gait simulation videos, corresponding to five subject models, five biomechanical objectives, and three visual perspectives. Subject models were constructed for five individuals performing straight line walking, with optimal tracking simulations generated for each using computed muscle control. Direct collocation was used to apply five different objectives to each individual's nominal behavior including metabolic cost, summed and squared muscle activations, time-integrated whole-body angular momentum, time-integrated bilateral ground reaction forces, and an equally weighted multi-objective cost function summing the individual objectives. 100 crowd workers characterized each simulation on a 1-5 scale using stylistic labels corresponding to each objective. Multinomial logistic regression analysis revealed that loading and activation ratings were significant predictors of muscle activation-optimized movements, while activation ratings were significant predictors of movement perspective. Balance ratings were significant for the frontal view alone, suggesting that balance indicators are more easily distinguished in the frontal plane. Collectively, the wisdom of crowds could distinguish motion associated with some biomechanical objectives, but due to the redundancy of motor control strategies used by individuals, the resolution of this observational approach is limited.

Midline shift (MLS) is a vital diagnostic marker for brain pathologies such as large-territory infarction, intracranial hemorrhage, and traumatic brain injury, all of which require emergency treatment. Conventional imaging techniques like computed tomography (CT) and magnetic resonance imaging (MRI) are the gold standards for MLS measurement due to their high spatial resolution. However, these modalities prevent repeated, short-interval monitoring and pose significant risks when transporting critically ill patients to imaging suites. This feasibility study investigates a novel freehand 3D ultrasound (US) system for bedside MLS measurement, which aims to mitigate these risks while enabling continuous monitoring. <br/> <br/> Our system utilizes electromagnetic (EM) position tracking sensors attached to both the US transducer and the subject to accurately track the positions of individual frames relative to each other. By reconstructing 2D B-mode ultrasound images into a 3D volume, we enable visualization of arbitrary 2D slices in different planes, overcoming the limitations of traditional 2D ultrasound. <br/> <br/> We scanned 1 healthy subject and 2 neurocritical patients through bilateral transtemporal windows, acquiring 900–1200 B-mode frames per scan. MLS was measured by extracting axial slices from the reconstructed volume using Brain Trauma Foundation guidelines. Our system demonstrated a mean measurement difference of 0.4 mm compared to CT/MRI, with a Pearson correlation coefficient of r = 0.9998 (p = 0.0126), confirming near-perfect agreement with standard-of-care imaging, thus proving its sensitivity and accuracy for clinical use. <br/> <br/> Our freehand 3D US approach offers several advantages: improved usability, reduced operator-skill dependency, and a rapid, non-ionizing solution suitable for continuous, short-interval monitoring of MLS. This system can potentially enhance clinical decision-making and patient outcomes in critical care and emergency environments by providing a reliable and portable solution for monitoring brain midline shifts.

Manipulating deformable objects in robotic cells is often costly and not widely accessible. However, the use of localized pneumatic gripping systems can enhance accessibility. Current methods that use pneumatic grippers to handle deformable objects struggle with effective lifting. This paper introduces a method for the dexterous lifting of textile deformable objects from one edge, utilizing a previously developed gripper designed for flexible and porous materials. By precisely adjusting the orientation and position of the gripper during the lifting process, we were able to significantly reduce necessary gripping force and minimize object vibration caused by airflow. This method was tested and validated on four materials with varying mass, friction, and flexibility. The proposed approach facilitates the lifting of deformable objects from a conveyor or automated line, even when only one edge is accessible for grasping. Future work will involve integrating a vision system to optimize the manipulation of deformable objects with more complex shapes.

Twin-to-Twin Transfusion Syndrome (TTTS) is a life-threatening condition that occurs in monochorionic twin pregnancies, where one twin receives more blood flow from the shared placenta, while the other twin receives less. This imbalance can lead to cardiac failure, and in severe cases, even death of one or both twins. TTTS is treatable by a modified fetoscopic laser photocoagulation (FLP) of placental anastomoses. However, this procedure is challenging with conventional rigid instruments, especially for accessing the anterior placenta. These instruments have limited degrees of freedom hindering their ability to navigate the expansive uterine cavity and increasing movement around the amniotic membrane which could lead to Premature Rupture of Membranes (PROM) before labor begins. In this paper, we present a novel continuum manipulator inspired by human condyloid joints. The design has the following advantages: (1) a flexion range of up to 230°, (2) capability for omnidirectional bending, (3) torsion prevention with its ellipsoidal ring shape, and (4) a higher bending resolution and compliance compared to other rolling joint manipulators. The manipulator designed is a scaled-up prototype intended for miniaturization to fit a 10 French (3.3 mm outer diameter) trocar. We successfully demonstrate the performance and feasibility of the manipulator for TTTS surgery by testing its bending range and workspace volume.

Twin-to-twin transfusion syndrome (TTTS) surgical training, which involves a complex minimally invasive laser ablation therapy, is notably challenging due to the lack of high-fidelity simulators that can accurately replicate the in-utero environment. This paper presents an advanced TTTS surgery simulation developed in Unity incorporating visuo-haptic feedback via GeoMagic Touch and HoloLens assistance. Six training assistance cases were tested: (1) No Assistance, (2) Haptic Guidance, (3) Visual Guidance, (4) Mini-Map Guidance, (5) Combined Guidance (Visual, Haptic, and Mini-Map Guidance), and (6) HoloLens Guidance. In our study, thirty novice subjects were separated equally into the six assistance cases, and one expert surgeon tested on all six assistance cases. The participants performed three tasks: identifying anastomosis sites, laser ablating the sites, and creating a laser path between the points. Our results show that task efficiency and velocity of the laser ablation show statistical significant differences (p-value<0.05) between the no assistance and assistance cases. Moreover, there is an improvement of using the assistance cases for anastomosis marking and path ablation. Our simulation presents a significant advancement in TTTS surgery training by providing a realistic, hands-on platform for practitioners to enhance their skills.

Abstract Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, non-invasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.

Trocar insertion is a critical first step of all minimally invasive surgery; however, it also carries a high risk for errors. Studies suggest that entry errors are the most common complication in laparoscopic surgery with 4% of errors leading to patient fatality. Surgeon error due to excessive force is often the cause for entry errors; however, adequate training has been shown to reduce the risk of these surgical errors. In practice, institutions lack widespread and relatively inexpensive means to train surgeons for trocar entry that does not involve patient risk. In our prior work, we presented a simple Stewart platform haptic device with a numerical model to simulate key force characteristics of trocar insertion. Evaluation in our first study was limited to device characterization. In this paper, we present a more robust haptic mechanism with higher fidelity linear actuators, an increased workspace, and tissue visualization to accompany haptic cues. We also present a novel upper module that allows for a sudden drop of the trocar after the final puncture event to create a more realistic simulation. We performed a user study with eight novices to investigate how well the device and visualization train users in the trocar insertion procedure. By the end of the experiment, subjects using the device had a normalized error reduction of roughly 85% on average, relative to themselves. This device shows potential for widespread training of trocar insertion, possibly leading to fewer complications and deaths following the procedure. Finally, our upper module also represents an innovative addition for traditional admittance-type haptic device designs, not typically capable of accurately representing motion in free space.

Manipulation of soft tissues remains one of the most prevalent tasks during minimally-invasive surgery. How-ever, surgical instruments for soft tissue grasping are primarily composed of hard, metallic materials which could result in tissue damage, if not properly used, as well as require large contact areas, both locally during grasping, as well as across the entire organ during large manipulation tasks (e.g., running bowel). As an alternative to traditional pinch-type graspers for tissue manipulation, vortex technology, currently found in manufacturing applications with fragile materials, could hold great promise. Vortex technology creates a negative pressure vortex through the ejection of compressed air through a specialized nozzle. This technology enables low-contact grasping of materials, including those at a distance and even through levitation of objects. Unlike suction cups, vortex grippers are also capable of grasping complex, non-smooth surfaces. In this paper, we present the design of a vortex gripper for soft tissue manipulation. We investigate the force characteristics of this gripper in grasping soft objects with four common shapes of varying radii of curvature and also evaluate the gripper's performance in grasping bio-inspired, artificial intestine tissue. Finally, the paper concludes with proposed design enhance-ments for future use in applications with biological tissue.

Needle decompression is an urgent medical task to remove excess air in the chest, often performed by individuals who lack training. Inspired by prior work, our sleeve uses our best performing 3D guidance cue strategy to date, Cartesian feedback, to give online directional cues in 3D space. The tactile cues are felt as strokes in the left, right, forward, and backward direction, or pulses on the top or bottom of the hand to indicate down or up motion, respectively. The goal of this study is to evaluate if haptic cues given in an online way could outperform verbal cues for needle placement. Our between-subjects experiment revealed that verbal cues in fact outperformed the tactile guidance cues, having respective median error values of 1.00 mm and 1.17 mm between the mentor and trainee positions. These results indicate that the efficacy of simple Cartesian tactile guidance reduces when it is applied in an online way, specifically in the case of simple desired motions in Cartesian space.

Implicit integrators are lauded in Computer Graphics research for their stability and have been demonstrated to result in stable and responsive haptic feedback. However, the commonly used first-order Backward Difference Formula (BDF1) results in significant numerical damping and consequential inaccuracies of the resulting dynamics. Higher-order implicit integrators remedy this with lower energy dissipation at a higher computational cost. In this paper we examine the use of multiple higher-order integrators and their effect on virtual coupling force feedback during a god-object haptic simulation. We assess the free-space force as well as the force during contact and a penalty based collision response. In addition, we utilize a novel mesh processing technique which allows easily parallelizable collision detection and a higher contact fidelity. Our method is implemented using a Lambda.7 haptic device and our results show that using higher order integrators result in a more energy conservative response and preservation of high frequency dynamics. This is shown using our contact mesh but generalize to typical polygonal mesh representation.

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.

Enhancing human user performance in some complex task is an important research question in many domains from skilled manufacturing to rehabilitation and surgical training. Many examples in the literature explore the effects of both haptic assistance or guidance to complete a task, as well as haptic hindrance to temporarily increase task difficulty for the ultimate goal of faster learning. Studies also suggest adaptively changing guidance based on expertise may be most effective. However, to our knowledge, there has not yet been a conclusive study evaluating these enhancement modes in a systematic experiment. In this article, we evaluate learning outcomes for 24 human subjects in a randomized control trial performing a Fitt's law reaching task under various haptic feedback conditions including: no haptics, assistive haptics, resistive haptics, and adaptively changing haptics tied to current performance measures. Subjects each performed 400 trials total and this paper reports results for 40 pre-test and 40 post-test trials. While most conditions did show improvements in performance, we found statistically significant results indicating that our adaptive haptic feedback condition leads to faster and more effective learning as evidenced by metrics of movement time, overshoot, performance index, and speed when compared to the other groups.

While manipulation of rigid objects in the context of automated manufacturing has been well studied, there are few examples in the literature on how to handle the manipulation of nonrigid objects. One of the fundamental control challenges in manipulating nonrigid objects (e.g., films and fabrics) centers on the fact that the object inherently deforms during grasping and the object's center of mass also changes during manipulation. While pneumatic grippers are typically used in manufacturing applications for nonrigid objects today, their use remains fundamentally limited to planar manipulation due to the aforementioned challenges. In this article, we introduce a new approach for manipulating nonrigid objects by leveraging the effects of friction, reorientation, and a novel pneumatic gripper. We show that holding force increases with an increase in the orientation angle due to both reduction of depressurization within the suction cup, as well as the ability to leverage friction forces between the object and the gripper. The additional frictional forces present at the 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> case are also determined theoretically and experimentally for various films and fabrics. The paper further demonstrates the effectiveness of using reorientation up to an angle of 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> degrees during manipulation of nonrigid materials in the vertical direction. This article is the first step towards future work in real-time robotic controlled reorientation of nonrigid objects during dexterous manipulation.

Often in the field of haptic guidance, an important question is how the robotic device should assist some imperfect human movement. While many control strategies have been suggested to help improve human performance in particular tasks, structuring guidance in a generalizable way remains elusive. Many assistive controllers rely on predicting a user's goal movement or knowing some idealized trajectory a-priori but may fail to assist during an arbitrary task. In this study, we propose a ‘flipped’ approach to studying human-robot collaborative behavior - we ask humans to assist a robotic device whose movements are in some way imperfect. We conducted an experiment during which subjects assisted a haptic device performing a shape-following task autonomously but with different types of error-prone controllers. For each shape, we evaluated a simple trajectory-following controller as well as one with human-like motion constraints. We also evaluated the role of visual feedback on a user's ability to help the robot accomplish the unknown task. We found that the human was generally able to improve the robotic error in all trajectories when the robotic motion did not include gravity compensation; however, error reduction was primarily in the vertical direction. When the robotic controller included gravity compensation, the human user was not able to improve errors significantly, except for vertical errors when provided visual feedback. In the no visual feedback conditions, the human user contributed to significantly greater error for most paths compared to a robot with gravity compensation, indicating an inability to provide assistance in that case.

In teleoperated robots, such as surgical robots, there is a desire to infer the intent of the operator and provide assistance as needed. This lofty goal is especially challenging when it comes to long-horizon inference. In this paper, we propose leveraging a Transformer-based model to predict the long-horizon trajectory of the master-side manipulators of the da Vinci surgical robot, while also investigating the role of trajectory-based haptic guidance cues as potentially assistive cues. Using the JIGSAW dataset, our model achieved an RMSE Cartesian error of 26.14mm when using the provided gesture labels and 32.13mm without gesture labels for master-side manipulators 1-second-ahead trajectory prediction. We then created resistive and assistive haptic guidance cues with a virtual spring between the current manipulator position and prior or future predicted positions, respectively. Each condition consisted of two levels, defined by 0.5s and Is time horizons. We conducted a preliminary human subject study with 10 subjects to investigate the role of these guidance forces on completion time for a running suturing task. While there are no statistically significant time differences based on type of haptic cue and time-horizon, we observed that the long-horizon resistive guidance had weak significance to improve the mean task performance in a washout trial that immediately followed the haptic condition. We also observed a large decrease in user difficulty ratings for this trial. These results indicate that haptic guidance cues could be leveraged in surgical training, potentially resulting in lasting after-effects on performance once the guidance has been removed.

We propose a novel Augmented Reality (AR) Head Mounted Display (HMD) haptic-enabled device which is capable of providing visual and vibrotactile directional cues to locate objects of interest. Using the vibrotactile cues, the device communicates prioritization information to users without the need for additional graphics. This work builds upon a human-robot teaming AR application, AugRE, which provides both situational awareness and control interfaces for any number of ROS-enabled robotic systems. The vibrotactile haptic component developed attaches to the AR-HMD and uses a sequence of vibrations to direct the user to specific objects in their proximity. The visual haptic component does the same by overlaying a holographic arrow on the HMD. We present results from a pilot study and discuss system limitations and research areas that may help direct future development for human-robot teaming applications. Results indicate that visual haptic cues provide the best response times. However, high-frequency vibrotactile haptic cues may be a viable alternative for some tasks where the visual space is already saturated.

Abstract Handling and manipulating flexible porous objects is one of the main challenges in robotics for household and industrial tasks. Improving the design of grippers for flexible objects of manipulation is an important stage in the development of this topic. This article proposes a method of modeling a gripper for porous objects using the finite element method. It identifies the main parameters of the model that will affect the grasping force and the permeability of porous objects. The power characteristics of the obtained gripper model for different supply pressures, with varying porosity of the manipulated objects, are determined. The obtained characteristics are then used to find the correspondence of channel length for three textile materials with different permeable properties. An experimental study of the lifting force is conducted, and a comparison is made with the obtained modeling data for the presented samples. Additionally, using the obtained simulation data, an analysis of the pressure distribution on the surface of the porous object of manipulation is performed. As a result, it is found that the gripping device must use a design with elements to stabilize the distribution of pressure in its chamber, which will increase the stability of the gripping process.

Haptic feedback can enhance training and performance of human operators; however, the design of haptic feedback for bimanual coordination in robot-assisted tasks (e.g., control of surgical robots) remains an open problem. In this study, we present four bimanually-dependent haptic force feedback conditions aimed at shaping bimanual movement according to geometric characteristics: the number of targets, direction, and symmetry. Haptic conditions include a virtual spring, damper, combination spring-damper, and dual springs placed between the hands. We evaluate the effects of these haptic conditions on trajectory shape, smoothness, and speed. We hypothesized that for subjects who perform worse with no haptic feedback ❨1❩ a spring will improve the shape of parallel trajectories, ❨2❩ a damper will improve the shape of point symmetric trajectories, ❨3❩ dual springs will improve the shape of trajectories with one target, and ❨4❩ a damper will improve smoothness for all trajectories. Hypotheses ❨1❩ and ❨2❩ were statistically supported at the p < 0.001level, but hypotheses ❨3❩ and ❨4❩ were not supported. Moreover, bimanually-dependent haptic feedback tended to improve shape accuracy for movements that subjects performed worse on under no haptic condition. Thus, bimanual haptic feedback based on geometric trajectory characteristics shows promise to improve performance in robot-assisted motor tasks.

With the evolution of hand-based haptic interfaces, novel forms of controlled force feedback arise allowing multipoint interaction between virtual objects and the hand’s digits. With these advances, there must be an effective force display coupled with an intuitive visualization of the hand at its points of high manipulability. This is the basis for dexterous manipulation immersion in virtual environments. Still, there are challenges due to the complexity of force interaction, bandwidth limitations, and redundant visual configurations. In this paper, we present an extended proxy algorithm for digit-based interactions, which through configuration-based optimization, provides an efficient, robust, and visually plausible way to interact with virtual objects with a virtual hand. Additionally, we revisit a seldom-seen modality of haptic rendering, whole-hand kinesthetic feedback, with the Maestro exoskeleton in the implementation of our algorithm. We unify these methods and develop a CHAI3D module in a comprehensive visuo-haptic framework that was evaluated through demonstrations of joint-level haptic force data during interaction with static and dynamic objects alike. Our computationally-efficient approach sets the foundation for the visual display of in-hand virtual object manipulation with the effective rendering of stable haptic interactions under complex tasks.

Gripping and manipulating objects using non-contact and low-contact technologies is becoming increasingly necessary in manufacturing. One of the promising contactless gripping technologies is Bernoulli gripping devices for industrial robots. They have many advantages, but when changing the nozzle geometry, it is difficult to find the optimal parameters of the outer diameter of the gripper and its operating parameters. Therefore, the article presents a model for numerical simulation of the dynamics of airflow in the nozzle of the Bernoulli gripping device and in the radial gap between its active surface and the surface of the object of manipulation. Reynolds-averaged Navier–Stokes equations of viscous gas dynamics, SST-model of turbulence, and γ-model of laminar-turbulent transition were used for this purpose. The technical requirements for the design of the nozzle of Bernoulli jet gripping nozzles are determined and variants of their constructive improvement are offered. According to the results of numerical simulation in the Ansys-CFD software environment, the optimal diameter of the Bernoulli gripping device and the influence of the geometric parameters of the nozzle on the nature of the pressure distribution in the radial gap and its lifting force were determined. Determined the optimal parameters of the height of the gap between the object of manipulation and the Bernoulli gripping device using C—Factor, which will allow efficient operation of Bernoulli gripping devices during automated handling operations using industrial robots.

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.05$ </tex-math></inline-formula> , which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p &lt; 0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

Visualization of hand movement is an important part of many medical training, VR and haptic experiences, which researchers typically address by developing application specific hand visualization tools. While some existing simulators allow for hand kinematic visualization using a generic hand model, they are usual targeted for robotic grasp planning rather than designed specifically for rendering in applications that include haptic experiences. To fill this gap, in this paper we present cHand, an extension of the haptics software library CHAI3D, that enables hand kinematic visualization of an arbitrary hand model. A representation of the hand can be achieved with elementary geometric shapes that are provided by CHAI3D, or with custom geometries loaded from STL files. We release cHand as an open source contribution to keep with the open source nature of CHAI3D, and present a tutorial on its use in this manuscript.