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Interactively teaming up animals and robots could facilitate basic scientific research and address environmental and ecological crises.

Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules and materials to unlock capabilities in energy, catalysis, biotechnology, sustainability, electronics, and more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis and characterization tasks. Robotics and automation can enable scientific experiments to be conducted faster, more safely, more accurately, and with greater reproducibility, allowing scientists to tackle large societal problems in domains such as health and energy on a shorter timescale. We define five levels of laboratory automation, from laboratory assistance to full automation. We also introduce robotics research challenges that arise when increasing levels of automation and when increasing the generality of tasks within the laboratory. Robots are poised to transform science labs into automated factories of discovery that accelerate scientific progress.

The recent movie Atlas misses fundamental robotics advances in self-stabilization and human-robot interaction.

In the amusing 1982 novel Software , robots punish their human overlords by raising prices on longevity drugs and organ transplants.

The OpenFlexure Microscope is an accessible, three-dimensional-printed robotic microscope, with sufficient image quality to resolve diagnostic features including parasites and cancerous cells. As access to lab-grade microscopes is a major challenge in global healthcare, the OpenFlexure Microscope has been developed to be manufactured, maintained and used in remote environments, supporting point-of-care diagnosis. The steps taken in transforming the hardware and software from an academic prototype towards an accepted medical device include addressing technical and social challenges, and are key for any innovation targeting improved effectiveness in low-resource healthcare. This article is part of the Theo Murphy meeting issue 'Open, reproducible hardware for microscopy'.

To enhance wearable robots, understanding user intent and environmental perception with novel vision approaches is needed.

Background: As the disabled population ages and the demand for care increases, Socially, the need for care robots is emerging but, perceptions of care robots among care recipients is unknown., Purpose: To determine the level of intention to use care robots among care recipients and identify predictors of intention to use care robots., Methods: A cross-sectional survey was conducted using a convenience sample of 163 persons with disabilities from January to March 2022 at the Veterans Health Service Medical Center., Discussion: Overall, 64.42% of respondents intended to use care robots. Predictors included perceived behavioral control, participants' perceptions of the caregiver's burden, attitude toward robot use, subjective norms, and age., Conclusion: These findings suggest that individuals who are community-dwelling desire the use care robots to maintain their independence and may provide useful insignt for the introduction various care robots in acute care and long-term care settings., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had severe consequences for health and the global economy. To control the transmission, there is an urgent demand for early diagnosis and treatment in the general population. In the present study, an automatic system for SARS-CoV-2 diagnosis is designed and built to deliver high specification, high sensitivity, and high throughput with minimal workforce involvement. The system, set up with cross-priming amplification (CPA) rather than conventional reverse transcription-polymerase chain reaction (RT-PCR), was evaluated using more than 1000 real-world samples for direct comparison. This fully automated robotic system performed SARS-CoV-2 nucleic acid-based diagnosis with 192 samples in under 180 min at 100 copies per reaction in a "specimen in data out" manner. This throughput translates to a daily screening capacity of 800-1000 in an assembly-line manner with limited workforce involvement. The sensitivity of this device could be further improved using a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based assay, which opens the door to mixed samples, potentially include SARS-CoV-2 variants screening in extensively scaled testing for fighting COVID-19., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, using exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step or are missing altogether due to high reflectance. In addition, depth perception can degrade due to difficult lighting, dust, fog, reflective or transparent surfaces, sensor occlusion, and more. For this reason, the most robust and general solutions to legged locomotion to date rely solely on proprioception. This severely limits locomotion speed because the robot has to physically feel out the terrain before adapting its gait accordingly. Here, we present a robust and general solution to integrating exteroceptive and proprioceptive perception for legged locomotion. We leverage an attention-based recurrent encoder that integrates proprioceptive and exteroceptive input. The encoder is trained end to end and learns to seamlessly combine the different perception modalities without resorting to heuristics. The result is a legged locomotion controller with high robustness and speed. The controller was tested in a variety of challenging natural and urban environments over multiple seasons and completed an hour-long hike in the Alps in the time recommended for human hikers.

Understanding the human motor control strategy during physical interaction tasks is crucial for developing future robots for physical human-robot interaction (pHRI). In physical human-human interaction (pHHI), small interaction forces are known to convey their intent between the partners for effective motor communication. The aim of this work is to investigate what affects the human's sensitivity to the externally applied interaction forces. The hypothesis is that one way the small interaction forces are sensed is through the movement of the arm and the resulting proprioceptive signals. A pHRI setup was used to provide small interaction forces to the hand of seated participants in one of four directions, while the participants were asked to identify the direction of the push while blindfolded. The result shows that participants' ability to correctly report the direction of the interaction force was lower with low interaction force as well as with high muscle contraction. The sensitivity to the interaction force direction increased with the radial displacement of the participant's hand from the initial position: the further they moved the more correct their responses were. It was also observed that the estimated stiffness of the arm varies with the level of muscle contraction and robot interaction force., (© 2021. The Author(s).)

Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating tasks such as food handling, garment sorting, or assistive dressing requires open problems of modeling, perceiving, planning, and control to be solved. Recent advances in data-driven approaches, together with classical control and planning, can provide viable solutions to these open challenges. In addition, with the development of better simulation environments, we can generate and study scenarios that allow for benchmarking of various approaches and gain better understanding of what theoretical developments need to be made and how practical systems can be implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey more than 100 relevant studies in this area and use it as the basis to discuss open problems. We adopt a learning perspective to unify the discussion over analytical and data-driven approaches, addressing how to use and integrate model priors and task data in perceiving and manipulating a variety of deformable objects., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background and Aim: Loneliness is a common problem in older adults and contributes to poor health. This scoping review aimed to synthesize and report evidence on the effectiveness of interventions using social robots or computer agents to reduce loneliness in older adults and to explore intervention strategies., Methods: The review adhered to the Arksey and O'Malley process for conducting scoping reviews. The SCOPUS, PUBMED, Web of Science, EMBASE, CINAHL, PsycINFO, ACM Digital Library and IEEE Xplore databases were searched in November, 2020. A two-step selection process identified eligible research. Information was extracted from papers and entered into an Excel coding sheet and summarised. Quality assessments were conducted using the Mixed Methods Appraisal Tool., Results: Twenty-nine studies were included, of which most were of moderate to high quality. Eighteen were observational and 11 were experimental. Twenty-four used robots, four used computer agents and one study used both. The majority of results showed that robots or computer agents positively impacted at least one loneliness outcome measure. Some unintended negative consequences on social outcomes were reported, such as sadness when the robot was removed. Overall, the interventions helped to combat loneliness by acting as a direct companion (69%), a catalyst for social interaction (41%), facilitating remote communication with others (10%) and reminding users of upcoming social engagements (3%)., Conclusion: Evidence to date suggests that robots can help combat loneliness in older adults, but there is insufficient research on computer agents. Common strategies for reducing loneliness include direct companionship and enabling social interactions. Future research could investigate other strategies used in human interventions (eg, addressing maladaptive social cognition and improving social skills), and the effects of design features on efficacy. It is recommended that more robust experimental and mixed methods research be conducted, using a combination of validated self-report, observational, and interview measures of loneliness., Competing Interests: Elizabeth Broadbent authored/co-authored five studies included in the review. However, Elizabeth did not conduct the quality assessments, so potential conflicts of interest were mitigated. Soul Machines Ltd (a company that makes digital humans) employs Kate Loveys as an intern and contracts Elizabeth Broadbent as a consultant. The authors report no other conflicts of interest in this work., (© 2021 Gasteiger et al.)

The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand's actuation input to the object's motion to be obtained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic reasoning and, in turn, show that the self-identified VLR can enable the control of precise in-hand manipulation. To validate our framework, we instantiated the proposed system on a Yale Model O hand without joint encoders or tactile sensors. The passive adaptability of the underactuated hand greatly facilitates the self-identification process, because they naturally secure stable hand-object interactions during random exploration. Relying solely on an in-hand camera, our system can effectively self-identify the VLRs, even when some fingers are replaced with novel designs. In addition, we show in-hand manipulation applications of handwriting, marble maze playing, and cup stacking to demonstrate the effectiveness of the VLR in precise in-hand manipulation control., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Policy gradient methods can be used for mechanical and computational co-design of robot manipulators., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The ability to grab, hold, and manipulate objects is a vital and fundamental operation in biological and engineering systems. Here, we present a soft gripper using a simple material system that enables precise and rapid grasping, and can be miniaturized, modularized, and remotely actuated. This soft gripper is based on kirigami shells-thin, elastic shells patterned with an array of cuts. The kirigami cut pattern is determined by evaluating the shell's mechanics and geometry, using a combination of experiments, finite element simulations, and theoretical modeling, which enables the gripper design to be both scalable and material independent. We demonstrate that the kirigami shell gripper can be readily integrated with an existing robotic platform or remotely actuated using a magnetic field. The kirigami cut pattern results in a simple unit cell that can be connected together in series, and again in parallel, to create kirigami gripper arrays capable of simultaneously grasping multiple delicate and slippery objects. These soft and lightweight grippers will have applications in robotics, haptics, and biomedical device design., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent. The neural agent can read sensors and control the angles and interactions between individual amino acids. During the training phase, the agent uses reinforcement learning to explore new folding forms that conduce toward more significant rewards. The memory of the agent is implemented with neural networks. These neural networks are noise-balanced trained to satisfy the look for future conditions required by the Bellman equation. In the operating phase, the components merge into a wise up protein folding robot with look-ahead capacities, which consistently solves a section of the HEs protein., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns associated with traffic congestion increase when considering more complicated situations such as unsignalized intersections and driveways at which maneuvers are entirely dependent upon drivers' judgment. Urban arterials are characterized by closely spaced signalized and unsignalized intersections and high traffic volumes, which make them a priority while analyzing traffic safety and operation. Autonomous Vehicles (AV) provide ample opportunities to overcome the aforementioned challenges. In essence, this study evaluates the impact of various AV Market Penetration Rates (MPR) on the safety and operation of urban arterials in proximity of a driveway under different traffic levels of service (LOS). Twenty-four separate scenarios were developed using VISSIM, considering six AV MPRs of 0 %, 10 %, 25 %, 50 %, 75 %, and 100 %, and four LOS including A, B, C, and D. Various operational and safety measures were analyzed including traffic density, traffic speed, traffic conflict (rear-end and lane-changing), and driving volatility. The trajectory and lane-based analysis of the traffic density indicates that MPR significantly improves the overall traffic density for all the scenarios, especially under high traffic LOS. Additionally, by increasing the MPR and decreasing the traffic volume of the network, the mean speed increases significantly by up to 6 %. Exploring the safety of the scenarios indicates that by increasing the MPR from 0% to 100 % for all the LOS, the number of rear-end conflicts and lane-changing conflicts decreases 84 %-100 % and 42 %-100 %, respectively. Moreover, assessing the longitudinal driving volatility measures, which represent risky driving behaviors, showed that higher MPRs significantly reduce some of the driving volatility measures and enhance safety., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Robot-assisted therapy and noninvasive brain stimulation (NIBS) are promising strategies for stroke rehabilitation., Objective: This systematic review and meta-analysis aims to evaluate the evidence of NIBS as an add-on intervention to robotic therapy in order to improve outcomes of upper-limb motor impairment or activity in individuals with stroke., Methods: This study was performed according to the PRISMA Protocol and was previously registered on the PROSPERO Platform (CRD42017054563). Seven databases and gray literature were systematically searched by 2 reviewers, and 1176 registers were accessed. Eight randomized clinical trials with upper-limb body structure/function or activity limitation outcome measures were included. Subgroup analyses were performed according to phase poststroke, device characteristics (ie, arm support, joints involved, unimanual or bimanual training), NIBS paradigm, timing of stimulation, and number of sessions. The Grade-Pro Software was used to assess quality of the evidence., Results: A nonsignificant homogeneous summary effect size was found both for body structure function domain (mean difference [MD] = 0.15; 95% CI = -3.10 to 3.40; P = 0.93; I 2 = 0%) and activity limitation domain (standard MD = 0.03; 95% CI = -0.28 to 0.33; P = 0.87; I 2 = 0%)., Conclusions: According to this systematic review and meta-analysis, at the moment, there are not enough data about the benefits of NIBS as an add-on intervention to robot-assisted therapy on upper-limb motor function or activity in individuals with stroke.

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The biomechanics underlying the predatory strike of dragonfly larvae is not yet understood. Dragonfly larvae are aquatic ambush predators, capturing their prey with a strongly modified extensible mouthpart. The current theory of hydraulic pressure being the driving force of the predatory strike can be refuted by our manipulation experiments and reinterpretation of former studies. Here, we report evidence for an independently loaded synchronized dual-catapult system. To power the ballistic movement of a single specialized mouthpart, two independently loaded springs simultaneously release and actuate two separate joints in a kinematic chain. Energy for the movement is stored by straining an elastic structure at each joint and, possibly, the surrounding cuticle, which is preloaded by muscle contraction. As a proof of concept, we developed a bioinspired robotic model resembling the morphology and functional principle of the extensible mouthpart. Understanding the biomechanics of the independently loaded synchronized dual-catapult system found in dragonfly larvae can be used to control the extension direction and, thereby, thrust vector of a power-modulated robotic system., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Elasticity has been linked to the remarkable propulsive efficiency of pulse-jet animals such as the squid and jellyfish, but reports that quantify the underlying dynamics or demonstrate its application in robotic systems are rare. This work identifies the pulse-jet propulsion mode used by these animals as a coupled mass-spring-mass oscillator, enabling the design of a flexible self-propelled robot. We use this system to experimentally demonstrate that resonance greatly benefits pulse-jet swimming speed and efficiency, and the robot's optimal cost of transport is found to match that of the most efficient biological swimmers in nature, such as the jellyfish Aurelia aurita The robot also exhibits a preferred Strouhal number for efficient swimming, thereby bridging the gap between pulse-jet propulsion and established findings in efficient fish swimming. Extensions of the current robotic framework to larger amplitude oscillations could combine resonance effects with optimal vortex formation to further increase propulsive performance and potentially outperform biological swimmers altogether., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Many fish species gather by the thousands and swim in harmony with seemingly no effort. Large schools display a range of impressive collective behaviors, from simple shoaling to collective migration and from basic predator evasion to dynamic maneuvers such as bait balls and flash expansion. A wealth of experimental and theoretical work has shown that these complex three-dimensional (3D) behaviors can arise from visual observations of nearby neighbors, without explicit communication. By contrast, most underwater robot collectives rely on centralized, above-water, explicit communication and, as a result, exhibit limited coordination complexity. Here, we demonstrate 3D collective behaviors with a swarm of fish-inspired miniature underwater robots that use only implicit communication mediated through the production and sensing of blue light. We show that complex and dynamic 3D collective behaviors-synchrony, dispersion/aggregation, dynamic circle formation, and search-capture-can be achieved by sensing minimal, noisy impressions of neighbors, without any centralized intervention. Our results provide insights into the power of implicit coordination and are of interest for future underwater robots that display collective capabilities on par with fish schools for applications such as environmental monitoring and search in coral reefs and coastal environments., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. This paper presents new funding, publication, and scholarly network metrics and visualizations that were validated via expert surveys. The metrics and visualizations exemplify the emergence and convergence of three areas of strategic interest: artificial intelligence (AI), robotics, and internet of things (IoT) over the last 20 years (1998-2017). For 32,716 publications and 4,497 NSF awards, we identify their topical coverage (using the UCSD map of science), evolving co-author networks, and increasing convergence. The results support data-driven decision making when setting proper research and development (R&D) priorities; developing future S&T investment strategies; or performing effective research program assessment., Competing Interests: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NSWC Crane did not play a role in the study design, analysis, and decision to publish. The specific role of Jonathan Dilger is articulated in the ‘author contributions’ section. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Other authors were not employed by the NSWC Crane.

For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster ( Nephrops norvegicus ), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Localization algorithms applied to acoustic tags for tracking marine animals can also be used to localize marine robots., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adélie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

A multidrone path-planning algorithm enables drones to scout Adélie penguin colonies in Antarctica., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

In this paper, we developed a model that suggests the use of robots in identifying COVID-19-positive patients and which studied the effectiveness of the government policy of prohibiting migration of individuals into their countries especially from those countries that were known to have COVID-19 epidemic. Two compartmental models consisting of two equations each were constructed. The models studied the use of robots for the identification of COVID-19-positive patients. The effect of migration ban strategy was also studied. Four biologically meaningful equilibrium points were found. Their local stability analysis was also carried out. Numerical simulations were carried out, and the most effective strategy to curtail the spread of the disease was shown., Competing Interests: There is no conflict of interest related to this research., (Copyright © 2020 Isa Abdullahi Baba et al.)

It is generally accepted among biology and engineering communities that insects are unstable at hover. However, existing approaches that rely on direct averaging do not fully capture the dynamical features and stability characteristics of insect flight. Here, we reveal a passive stabilization mechanism that insects exploit through their natural wing oscillations: vibrational stabilization. This stabilization technique cannot be captured using the averaging approach commonly used in literature. In contrast, it is elucidated using a special type of calculus: the chronological calculus. Our result is supported through experiments on a real hawkmoth subjected to pitch disturbance from hovering. This finding could be particularly useful to biologists because the vibrational stabilization mechanism may also be exploited by many other creatures. Moreover, our results may inspire more optimal designs for bioinspired flying robots by relaxing the feedback control requirements of flight., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Batteries with conformal shape and multiple functionalities could provide new degrees of freedom in the design of robotic devices. For example, the ability to provide both load bearing and energy storage can increase the payload and extend the operational range for robots. However, realizing these kinds of structural power devices requires the development of materials with suitable mechanical and ion transport properties. Here, we report biomimetic aramid nanofibers-based composites with cartilage-like nanoscale morphology that display an unusual combination of mechanical and ion transport properties. Ion-conducting membranes from these aramid nanofiber composites enable pliable zinc-air batteries with cyclic performance exceeding 100 hours that can also serve as protective covers in various robots including soft and flexible miniaturized robots. The unique properties of the aramid ion conductors are attributed to the percolating network architecture of nanofibers with high connectivity and strong nanoscale filaments designed using a graph theory of composite architecture when the continuous aramid filaments are denoted as edges and intersections are denoted as nodes. The total capacity of these body-integrated structural batteries is 72 times greater compared with a stand-alone Li-ion battery with the same volume. These materials and their graph theory description enable a new generation of robotic devices, body prosthetics, and flexible and soft robotics with nature-inspired distributed energy storage., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Promoting health and prolonging independence in the home is a priority for older adults, caregivers, clinicians, and society at large. Rapidly developing robotics technology provides a platform for interventions, with the fields of physically and socially assistive robots expanding in recent years. However, less attention has been paid to using robots to enhance the cognitive health of older adults. The goal of this review is to synthesize the current literature on home-based cognitively assistive robots (CAR) in older adults without dementia and to provide suggestions to improve the quality of the scientific evidence in this subfield. First, we set the stage for CAR by: a) introducing the field of robotics to improve health, b) summarizing evidence emphasizing the importance of home-based interventions for older adults, c) reviewing literature on robot acceptability in older adults, d) highlighting important ethical issues in healthcare robotics, and e) reviewing current findings on socially assistive robots, with a focus on translating findings to the CAR context. With this foundation in place, we then review the literature on CAR, identifying gaps and limitations of current evidence, and proposing future directions for research. We conclude that CAR is promising and feasible and that there is a need for more methodologically rigorous evaluations of CAR to promote prolonged home-based independence in older adults., Competing Interests: No conflicts of interest were declared. The authors disclosed receipt of the following financial support for the authorship and/or publication of this article: Funding for this study was provided, in part, by the National Institutes of Health (grant R01MH094151-01 to D.V.J. [PI]), by the National Institute of Mental Health T32 Geriatric Mental Health Program (grant MH019934 to D.V.J. and E.W.T. [PIs]), the Stein Institute for Research on Aging at the University of California, San Diego., (© 2020 Van Patten et al.)

The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

Genetic control methods of mosquito vectors of malaria, dengue, yellow fever, and Zika are becoming increasingly popular due to the limitations of other techniques such as the use of insecticides. The sterile insect technique is an effective genetic control method to manage insect populations. However, it is crucial to release sterile mosquitoes by air to ensure homogeneous coverage, especially in large areas. Here, we report a fully automated adult mosquito release system operated from an uncrewed aerial vehicle or drone. Our system, developed and tested in Brazil, enabled a homogeneous dispersal of sterile male Aedes aegypti while maintaining their quality, leading to a homogeneous sterile-to-wild male ratio due to their aggregation in the same sites. Our results indicate that the released sterile males were able to compete with the wild males in mating with the wild females; thus, the sterile males were able to induce sterility in the native female population. The use of drones to implement the sterile insect technique will lead to improvements in areal coverage and savings in operational costs due to the requirement of fewer release sites and field staff., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background and Objectives: Dropped head syndrome (DHS) is characterized by apparent neck extensor muscle weakness and difficulty in extending the neck to raise the head against gravity. DHS affects forward vision and eating behavior, and hence impairs quality of life. However, standardized treatment of DHS has not yet been established. The purpose of this preliminary study is to seek for a possibility of effective non-surgical, conservative treatment for DHS, by applying a robotic treatment. Materials and Methods : A wearable exoskeleton type robot suit hybrid assistive limb (HAL) was applied to three patients with DHS. A course of HAL treatment included 10 sessions of gait training using HAL. One session lasted about an hour. Case 1 completed the course twice, the first time in two weeks (one session per day) and second time in 10 months (one session per month). Case 2 and Case 3 completed the course once in 10 weeks (one session per week) and in 6 months (one session per 2.5 weeks), respectively. Immediate and lasting effects of HAL on the reduction of cervical sagittal vertical alignment (SVA) during gait was evaluated using a motion capture system. Results : Case 1 showed improvement of cervical alignment during gait after the HAL courses of both different frequencies. Case 2 did not show improvement of cervical alignment during gait. Case 3 showed improvement of cervical kyphosis but not of cervical sagittal alignment during gait. Conclusions : The results of the preliminary study suggest that gait training using HAL may be an effective option of conservative treatment for a part of DHS patients. They also suggest that a lack of immediate effects on the cervical alignment and a lack of ability to perform compensatory trunk motion may indicate a non-responding patient. Generalization of the results requires further research with more cases., Competing Interests: Y.S. (Yoshiyuki Sankai) is the C.E.O., shareholder, and director of CYBERDYNE Inc. which produces the robot suit HAL. CYBERDYNE had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Flying insects have evolved to develop efficient strategies to navigate in natural environments. Yet, studying them experimentally is difficult because of their small size and high speed of motion. Consequently, previous studies were limited to tethered flights, hovering flights, or restricted flights within confined laboratory chambers. Here, we report the development of a cable-driven parallel robot, named lab-on-cables, for tracking and interacting with a free-flying insect. In this approach, cameras are mounted on cables, so as to move automatically with the insect. We designed a reactive controller that minimizes the online tracking error between the position of the flying insect, provided by an embedded stereo-vision system, and the position of the moving lab, computed from the cable lengths. We validated the lab-on-cables with Agrotis ipsilon moths (ca. 2 centimeters long) flying freely up to 3 meters per second. We further demonstrated, using prerecorded trajectories, the possibility to track other insects such as fruit flies or mosquitoes. The lab-on-cables is relevant to free-flight studies and may be used in combination with stimulus delivery to assess sensory modulation of flight behavior (e.g., pheromone-controlled anemotaxis in moths)., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

By adopting and extending lessons from the air traffic control system, we argue that a nationwide remote monitoring system for driverless vehicles could increase safety dramatically, speed these vehicles' deployment, and provide employment. It is becoming clear that fully driverless vehicles will not be able to handle "edge" cases in the near future, suggesting that new methods are needed to monitor remotely driverless vehicles' safe deployment. While the remote operations concept is not new, a super-human driver is needed to handle sudden, critical events. We envision that the remote operators do not directly drive the vehicles, but provide input on high level tasks such as path-planning, object detection and classification. This can be achieved via input from multiple individuals, coordinated around a task at a moment's notice. Assuming a 10% penetration rate of driverless vehicles, we show that one remote driver can replace 14,840 human drivers. A comprehensive nationwide interoperability standard and procedure should be established for the remote monitoring and operation of driverless vehicles. The resulting system has potential to be an order of magnitude safer than today's ground transportation system. We articulate a research and policy roadmap to launch this nationwide system. Additionally, this hybrid human-AI system introduces a new job category, likely a source of employment nationwide., Competing Interests: The authors have declared that no competing interests exist.

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Pingao Huang et al.)

For robots to be useful for real-world applications, they must be safe around humans, be adaptable to their environment, and operate in an untethered manner. Soft robots could potentially meet these requirements; however, existing soft robotic architectures are limited by their ability to scale to human sizes and operate at these scales without a tether to transmit power or pressurized air from an external source. Here, we report an untethered, inflated robotic truss, composed of thin-walled inflatable tubes, capable of shape change by continuously relocating its joints, while its total edge length remains constant. Specifically, a set of identical roller modules each pinch the tube to create an effective joint that separates two edges, and modules can be connected to form complex structures. Driving a roller module along a tube changes the overall shape, lengthening one edge and shortening another, while the total edge length and hence fluid volume remain constant. This isoperimetric behavior allows the robot to operate without compressing air or requiring a tether. Our concept brings together advantages from three distinct types of robots-soft, collective, and truss-based-while overcoming certain limitations of each. Our robots are robust and safe, like soft robots, but not limited by a tether; are modular, like collective robots, but not limited by complex subunits; and are shape-changing, like truss robots, but not limited by rigid linear actuators. We demonstrate two-dimensional (2D) robots capable of shape change and a human-scale 3D robot capable of punctuated rolling locomotion and manipulation, all constructed with the same modular rollers and operating without a tether., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user's data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: This study compares the impact of open (OIHR) versus laparoscopic (LIHR) inguinal hernia repair on healthcare spending and postoperative outcomes., Methods: The TRUVEN database was queried using ICD9 procedure codes for open, laparoscopic, and robotic-assisted IHR, from 2012 to 2013. Patients > 18 years of age and continuously enrolled for 12 months postoperatively were included. Demographics, patient comorbidities, postoperative complications, pain medication use, length of hospital stay, missed work hours, postoperative visits, and overall expenditure were collected, and assessed at time of surgery and at 30-, 60-, 90-, 180-, and 365-days postoperatively. Statistical analysis was conducted using SAS, with α = 0.05., Results: 66,116 patients were included (LIHR: N = 23,010; OIHR: N = 43,106). Robotic-assisted procedures were excluded due to small sample size (N = 61). The largest demographic was males between 55 and 64 years. LIHR had fewer surgical wound complications than OIHR (LIHR: 0.3%; OIHR: 0.5%, p = 0.007), less utilization of pain medication (LIHR: 23.3%; OIHR: 28.5%; p < 0.001), and fewer outpatient visits. In the 90-day postoperative period, LIHR had significantly fewer missed work hours (LIHR: 12.1 ± 23.2 h; OIHR: 12.9 ± 26.7 h, p = 0.023). LIHR had higher postoperative urinary complications (LIHR: 0.2%; OIHR: 0.1%; p < 0.001), consistent with the current literature. LIHR expenditures ($15,030 ± $25,906) were higher than OIHR ($13,303 ± 32,014), p < 0.001., Conclusions: The results highlight the benefits of laparoscopic repair with regard to surgical wound complications, postoperative pain, outpatient visits, and missed work hours. These improved outcomes with respect to overall healthcare spending and employee absenteeism support the paradigm shift toward laparoscopic inguinal hernia repairs, in spite of higher overall expenditures.

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence., Competing Interests: The authors have declared that no competing interests exist.

A robotic surgical system capable of performing minimally invasive surgery (MIS) is proposed in this paper. Based on the requirements of MIS, a compliant, seven- degrees of freedom (7-DOF) pneumatically actuated mechanism is designed. A remote center of motion (RCM) as a parallelogram mechanism for holding the laparoscopic camera is also developed. The operating workspace of robotic surgical system is determined considering the physical constraints imposed by mechanical joints. The simulation results show that the robotic system meets the design requirement. This research will lay a good foundation for the development of a compliant surgical robot to assist in MIS.

Industrial robots are a strategic future technology and an important part of the development of artificial intelligence, and they are a necessary means for the intelligent transformation of manufacturing industry. Based on global industrial robot trade data from 1998 to 2017, this paper applies the dynamic complex network analysis method to reveal the spatial and temporal variation characteristics and trade status evolution of the global industrial robot trade network. The results show that the global industrial robot network density has steadily increased, and the industrial robot trade has been characterized by 'diversification'. The number of major industrial robot exporters in the world is increasing, and the import market is increasingly diversified. The export market structure is relatively tight, the centrality of the global industrial robot trade network shows a downward trend, and the dissimilarity of the 'core-edge' clusters decreases year by year. The trade status of 'catch-up' countries represented by China has rapidly increased. However, Japan, Germany, and Italy are still in the central position of the industrial robot trade. Moreover, trade of the 'catch-up' countries' is dominated by imports, and exports of industrial robot products are insufficient. Finally, policy suggestions are provided according to the results., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR)., Methods: We followed a Wittgensteinian approach ("meaning by usage") applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts., Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated., Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

Microbot locomotion is challenging because of the reversible nature of microscale fluid flow, a limitation that can be overcome by breaking flowfield symmetry with a nearby surface. We have used this strategy with rotating wheel-shaped microbots, μwheels, that roll on surfaces leading to enhanced propulsion and fast translation speeds. Despite this, studies to date on flat surfaces show that μwheels roll inefficiently with significant slip. Taking inspiration from the mathematics of roads and wheels, here we demonstrate that μwheel velocities can be significantly enhanced by changing microroad topography. In this, we observe that periodic bumps in the road can be used to enhance the traction between μwheels and nearby walls. While continuous μwheel rotation with slip is observed on flat surfaces, a combination of rotation with slip and non-slip flip occurs when μwheels roll upon surfaces with periodic features, resulting in up to four-fold enhancement in translation velocity. The surprisingly fast rolling speed of μwheels on bumpy roads can be attributed to the hydrodynamic coupling between μwheels and road surface features, allowing non-slip rotation of entire wheels along one of their stationary edges. This road/wheel coupling can also be used to enhance μwheel sorting and separation where the gravitational potential energy barrier induced by topographic surfaces can lead to motion in only one direction and to different rolling speeds between isomeric wheels, allowing one to separate them not based on size but on symmetry., Competing Interests: Competing Interests: The authors declare that they have no competing interests.