Frontiers in Robotics and AI

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Dynamic locomotion of a quadruped robot emerges from interaction between the robot body and the terrain. When the robot has a soft body, dynamic locomotion can be realized using a simple controller. This study investigates dynamic turning of a soft quadruped robot by changing the phase difference among the legs of the robot. We develop a soft quadruped robot driven by McKibben pneumatic artificial muscles. The phase difference between the hind and fore legs is fixed whereas that between the left and right legs is changed to enable the robot to turn dynamically. Since the robot legs are soft, the contact pattern between the legs and the terrain can be varied adaptively by simply changing the phase difference. Experimental results demonstrate that changes in the phase difference lead to changes in the contact time of the hind legs, and as a result, the soft robot can turn dynamically.

Many species of termites build large, structurally complex mounds, and the mechanisms behind this coordinated construction have been a longstanding topic of investigation. Recent work has suggested that humidity may play a key role in the mound expansion of savannah-dwelling Macrotermes species: termites preferentially deposit soil on the mound surface at the boundary of the high-humidity region characteristic of the mound interior, implying a coordination mechanism through environmental feedback where addition of wet soil influences the humidity profile and vice versa. Here we test this potential mechanism physically using a robotic system. Local humidity measurements provide a cue for material deposition. As the analogue of the termite's deposition of wet soil and corresponding local increase in humidity, the robot drips water onto an absorbent substrate as it moves. Results show that the robot extends a semi-enclosed area outward when air is undisturbed, but closes it off when air is disturbed by an external fan, consistent with termite building activity in still vs. windy conditions. This result demonstrates an example of adaptive construction patterns arising from the proposed coordination mechanism, and supports the hypothesis that such a mechanism operates in termites.

The dielectric elastomer (DE) is a new kind of functional polymer that can be used as a smart actuator due to the large deformation induced by voltage excitation. Dielectric elastomer actuators (DEAs) are usually excited by dynamic voltages to generate alternating motions. DEAs are prone to premature breakdown failure during the dynamic excitation, while the research on the breakdown of DEAs under cyclic voltage excitation is still not fully revealed. In this paper, the dynamic breakdown behaviors of DEAs made from VHB4910 film were experimentally investigated. The factors affecting the breakdown behavior of DEAs under dynamic voltages were determined, and the relevant changing laws were summarized accordingly. The experimental results show that under dynamic voltage excitation, the critical breakdown voltage of DEAs were augmented slowly with voltage frequency and showed a substantial dispersion. In addition, the maximum cycle numbers before breakdown were significantly affected by voltage parameters (such as frequency, amplitude, waveform). Finally, the underlying mechanisms of breakdown under cyclic voltages were discussed qualitatively, a power-law equation was proposed to characterize the maximum cycle number for the dynamic breakdown of DEAs, and related parameters were fitted. This study provides a new path to predict the service life of DEAs under dynamic voltage.

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

Cranes are widely used in the field of construction, logistics, and the manufacturing industry. Cranes that use wire ropes as the main lifting mechanism are deeply troubled by the swaying of heavy objects, which seriously restricts the working efficiency of the crane and even cause accidents. Compared with the single-pendulum crane, the double-pendulum effect crane model has stronger nonlinearity, and its controller design is challenging. In this paper, cranes with a double-pendulum effect are considered, and their nonlinear dynamical models are established. Then, a controller based on the radial basis function (RBF) neural network compensation adaptive method is designed, and a stability analysis is also presented. Finally, the hardware-in-the-loop experimental results show that the neural network compensation control can effectively improve the control performance of the controller in practice.

In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.

Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop “smart” training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the efficacy of such metrics: how do these metrics relate to clinical skill, and how to best compute these metrics from sensor data and relate them with similar metrics? This study addresses these questions in the context of hemodialysis cannulation by enrolling 52 clinicians who performed cannulation in a simulated arteriovenous (AV) fistula. For clinical skill, results demonstrate that the objective outcome metric flash ratio (FR), developed to measure the quality of task completion, outperformed traditional skill indicator metrics (years of experience and global rating sheet scores). For computing motion smoothness metrics for skill assessment, we observed that the lowest amount of smoothing could result in unreliable metrics. Furthermore, the relative efficacy of motion smoothness metrics when compared with other process metrics in correlating with skill was similar for FR, the most accurate measure of skill. These results provide guidance for the computation and use of motion-based metrics for clinical skill assessment, including utilizing objective outcome metrics as ideal measures for quantifying skill.

Strong adhesion between hydrogels and various engineering surfaces has been achieved; yet, achieving fatigue-resistant hydrogel adhesion remains challenging. Here, we examine the fatigue of a specific type of hydrogel adhesion enabled by hydrogen bonds and wrinkling and show that the physical interactions–based hydrogel adhesion can resist fatigue damage. We synthesize polyacrylamide hydrogel as the adherend and poly(acrylic acid-co-acrylamide) hydrogel as the adhesive. The adherend and the adhesive interact via hydrogen bonds. We further introduce wrinkles at the interface by biaxially prestretching and then releasing the adherends and perform butt-joint tests to probe the adhesion performance. Experimental results reveal that the samples with a wrinkled interface resist fatigue damage, while the samples with a flat interface fail in ~9,000 cycles at stress levels of 70 and 63% peak stresses in static failure. The endurance limit of the wrinkled-interface samples is comparable to the peak stress of the flat-interface samples. Moreover, we find that the nearly perfectly elastic polyacrylamide hydrogel also suffers fatigue damage, which limits the fatigue life of the wrinkled-interface samples. When cohesive failure ensues, the evolutions of the elastic modulus of wrinkled-interface samples and hydrogel bulk, both in satisfactory agreements with the predictions of damage accumulation theory, are alike. We observe similar behaviors in different material systems with polyacrylamide hydrogels with different water contents. This work proves that physical interactions can be engaged in engineering fatigue-resistant adhesion between soft materials such as hydrogels.

Conceptual knowledge about objects is essential for humans, as well as for animals, to interact with their environment. On this basis, the objects can be understood as tools, a selection process can be implemented and their usage can be planned in order to achieve a specific goal. The conceptual knowledge, in this case, is primarily concerned about the physical properties and functional properties observed in the objects. Similarly tool-use applications in robotics require such conceptual knowledge about objects for substitute selection among other purposes. State-of-the-art methods employ a top-down approach where hand-crafted symbolic knowledge, which is defined from a human perspective, is grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, a robot's conceptual understanding of objects (e.g., light/heavy) will vary and therefore should be generated from the robot's perspective entirely, which entails robot-centric conceptual knowledge about objects. A similar bottom-up argument has been put forth in cognitive science that humans and animals alike develop conceptual understanding of objects based on their own perceptual experiences with objects. With this goal in mind, we propose an extensible property estimation framework which consists of estimations methods to obtain the quantitative measurements of physical properties (rigidity, weight, etc.) and functional properties (containment, support, etc.) from household objects. This property estimation forms the basis for our second contribution: Generation of robot-centric conceptual knowledge. Our approach employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising six physical and four functional properties of 110 household objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property estimation methods and the semantics of the considered properties within the dataset. Furthermore, the dataset includes the derived robot-centric conceptual knowledge using the proposed framework. The application of the conceptual knowledge about objects is then evaluated by examining its usefulness in a tool substitution scenario.

The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.

The rise of rehabilitation robotics has ignited a global investigation into the human machine interface (HMI) between device and user. Previous research on wearable robotics has primarily focused on robotic kinematics and controls but rarely on the actual design of the physical HMI (pHMI). This paper presents a data-driven statistical forearm surface model for designing a forearm orthosis in exoskeleton applications. The forearms of 6 subjects were 3D scanned in a custom-built jig to capture data in extreme pronation and supination poses, creating 3D point clouds of the forearm surface. Resulting data was characterized into a series of ellipses from 20 to 100% of the forearm length. Key ellipse parameters in the model include: normalized major and minor axis length, normalized center point location, tilt angle, and circularity ratio. Single-subject (SS) ellipse parameters were normalized with respect to forearm radiale-stylion (RS) length and circumference and then averaged over the 6 subjects. Averaged parameter profiles were fit with 3rd-order polynomials to create combined-subjects (CS) elliptical models of the forearm. CS models were created in the jig as-is (CS1) and after alignment to ellipse centers at 20 and 100% of the forearm length (CS2). Normalized curve fits of ellipse major and minor axes in model CS2 achieve R2 values ranging from 0.898 to 0.980 indicating a high degree of correlation between cross-sectional size and position along the forearm. Most other parameters showed poor correlation with forearm position (0.005 < R2 < 0.391) with the exception of tilt angle in pronation (0.877) and circularity in supination (0.657). Normalized RMSE of the CS2 ellipse-fit model ranged from 0.21 to 0.64% of forearm circumference and 0.22 to 0.46% of forearm length. The average and peak surface deviation between the scaled CS2 model and individual scans along the forearm varied from 0.56 to 2.86 mm (subject averages) and 3.86 to 7.16 (subject maximums), with the peak deviation occurring between 45 and 50% RS length. The developed equations allow reconstruction of a scalable 3D model that can be sized based on two user measures, RS length and forearm circumference, or based on generic arm measurements taken from existing anthropometric databases.

The COVID-19 pandemic has highly impacted the communities globally by reprioritizing the means through which various societal sectors operate. Among these sectors, healthcare providers and medical workers have been impacted prominently due to the massive increase in demand for medical services under unprecedented circumstances. Hence, any tool that can help the compliance with social guidelines for COVID-19 spread prevention will have a positive impact on managing and controlling the virus outbreak and reducing the excessive burden on the healthcare system. This perspective article disseminates the perspectives of the authors regarding the use of novel biosensors and intelligent algorithms embodied in wearable IoMT frameworks for tackling this issue. We discuss how with the use of smart IoMT wearables certain biomarkers can be tracked for detection of COVID-19 in exposed individuals. We enumerate several machine learning algorithms which can be used to process a wide range of collected biomarkers for detecting (a) multiple symptoms of SARS-CoV-2 infection and (b) the dynamical likelihood of contracting the virus through interpersonal interaction. Eventually, we enunciate how a systematic use of smart wearable IoMT devices in various social sectors can intelligently help controlling the spread of COVID-19 in communities as they enter the reopening phase. We explain how this framework can benefit individuals and their medical correspondents by introducing Systems for Symptom Decoding (SSD), and how the use of this technology can be generalized on a societal level for the control of spread by introducing Systems for Spread Tracing (SST).

With impressive developments in human–robot interaction it may seem that technology can do anything. Especially in the domain of social robots which suggest to be much more than programmed machines because of their anthropomorphic shape, people may overtrust the robot's actual capabilities and its reliability. This presents a serious problem, especially when personal well-being might be at stake. Hence, insights about the development and influencing factors of overtrust in robots may form an important basis for countermeasures and sensible design decisions. An empirical study [N = 110] explored the development of overtrust using the example of a pet feeding robot. A 2 × 2 experimental design and repeated measurements contrasted the effect of one's own experience, skill demonstration, and reputation through experience reports of others. The experiment was realized in a video environment where the participants had to imagine they were going on a four-week safari trip and leaving their beloved cat at home, making use of a pet feeding robot. Every day, the participants had to make a choice: go to a day safari without calling options (risk and reward) or make a boring car trip to another village to check if the feeding was successful and activate an emergency call if not (safe and no reward). In parallel to cases of overtrust in other domains (e.g., autopilot), the feeding robot performed flawlessly most of the time until in the fourth week; it performed faultily on three consecutive days, resulting in the cat's death if the participants had decided to go for the day safari on these days. As expected, with repeated positive experience about the robot's reliability on feeding the cat, trust levels rapidly increased and the number of control calls decreased. Compared to one's own experience, skill demonstration and reputation were largely neglected or only had a temporary effect. We integrate these findings in a conceptual model of (over)trust over time and connect these to related psychological concepts such as positivism, instant rewards, inappropriate generalization, wishful thinking, dissonance theory, and social concepts from human–human interaction. Limitations of the present study as well as implications for robot design and future research are discussed.

There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs.

The importance of embodiment for effective robot performance has been postulated for a long time. Despite this, only relatively recently concrete quantitative models were put forward to characterize the advantages provided by a well-chosen embodiment. We here use one of these models, based on the concept of relevant information, to identify in a minimalistic scenario how and when embodiment affects the decision density. Concretely, we study how embodiment affects information costs when, instead of atomic actions, scripts are introduced, that is, predefined action sequences. Their inclusion can be treated as a straightforward extension of the basic action space. We will demonstrate the effect on informational decision cost of utilizing scripts vs. basic actions using a simple navigation task. Importantly, we will also employ a world with “mislabeled” actions, which we will call a “twisted” world. This is a model which had been used in an earlier study of the influence of embodiment on decision costs. It will turn out that twisted scenarios, as opposed to well-labeled (“embodied”) ones, are significantly more costly in terms of relevant information. This cost is further worsened when the agent is forced to lower the decision density by employing scripts (once a script is triggered, no decisions are taken until the script has run to its end). This adds to our understanding why well-embodied (interpreted in our model as well-labeled) agents should be preferable, in a quantifiable, objective sense.

Virtual humans (VHs)—automated, three-dimensional agents—can serve as realistic embodiments for social interactions with human users. Extant literature suggests that a user’s cognitive and affective responses toward a VH depend on the extent to which the interaction elicits a sense of copresence, or the subjective “sense of being together.” Furthermore, prior research has linked copresence to important social outcomes (e.g., likeability and trust), emphasizing the need to understand which factors contribute to this psychological state. Although there is some understanding of the determinants of copresence in virtual reality (VR) (cf. Oh et al., 2018), it is less known what determines copresence in mixed reality (MR), a modality wherein VHs have unique access to social cues in a “real-world” setting. In the current study, we examined the extent to which a VH’s responsiveness to events occurring in the user’s physical environment increased a sense of copresence and heightened affective connections to the VH. Participants (N = 65) engaged in two collaborative tasks with a (nonspeaking) VH using an MR headset. In the first task, no event in the participant’s physical environment would occur, which served as the control condition. In the second task, an event in the participants’ physical environment occurred, to which the VH either responded or ignored depending on the experimental condition. Copresence and interpersonal evaluations of the VHs were measured after each collaborative task via self-reported measures. Results show that when the VH responded to the physical event, participants experienced a significant stronger sense of copresence than when the VH did not respond. However, responsiveness did not elicit more positive evaluations toward the VH (likeability and emotional connectedness). This study is an integral first step in establishing how and when affective and cognitive components of evaluations during social interactions diverge. Importantly, the findings suggest that feeling copresence with VH in MR is partially determined by the VHs’ response to events in the actual physical environment shared by both interactants.

There are currently many quadruped robots suited to a wide range of applications, but traversing some terrains, such as vertical ladders, remains an open challenge. There is still a need to develop adaptive robots that can walk and climb efficiently. This paper presents an adaptive quadruped robot that, by mimicking feline structure, supports several novel capabilities. We design a novel paw structure and several point-cloud-based sensory structures incorporating a quad-composite time-of-flight sensor and a dual-laser range finder. The proposed robot is equipped with physical and cognitive capabilities which include: 1) a dynamic-density topological map building with attention model, 2) affordance perception using the topological map, and 3) a neural-based locomotion model. The novel capabilities show strong integration between locomotion and internal–external sensory information, enabling short-term adaptations in response to environmental changes. The robot performed well in several situations: walking on natural terrain, walking with a leg malfunction, avoiding a sudden obstacle, climbing a vertical ladder. Further, we consider current problems and future development.

Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system’s resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.

There are high risks of infection for surgeons during the face-to-face COVID-19 swab sampling due to the novel coronavirus’s infectivity. To address this issue, we propose a flexible transoral robot with a teleoperated configuration for swab sampling. The robot comprises a flexible manipulator, an endoscope with a monitor, and a master device. A 3-prismatic-universal (3-PU) flexible parallel mechanism with 3 degrees of freedom (DOF) is used to realize the manipulator’s movements. The flexibility of the manipulator improves the safety of testees. Besides, the master device is similar to the manipulator in structure. It is easy to use for operators. Under the guidance of the vision from the endoscope, the surgeon can operate the master device to control the swab’s motion attached to the manipulator for sampling. In this paper, the robotic system, the workspace, and the operation procedure are described in detail. The tongue depressor, which is used to prevent the tongue’s interference during the sampling, is also tested. The accuracy of the manipulator under visual guidance is validated intuitively. Finally, the experiment on a human phantom is conducted to demonstrate the feasibility of the robot preliminarily.