A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

Perspective for soft robotics: the field's past and future

Since its beginnings in the 1960s, soft robotics has been a steadily growing field that has enjoyed recent growth with the advent of rapid prototyping and the provision of new flexible materials. These two innovations have enabled the development of fully flexible and untethered soft robotic systems. The integration of novel sensors enabled by new manufacturing processes and materials shows promise for enabling the production of soft systems with 'embodied intelligence'. Here, four experts present their perspectives for the future of the field of soft robotics based on these past innovations. Their focus is on finding answers to the questions of: how to manufacture soft robots, and on how soft robots can sense, move, and think. We highlight industrial production techniques, which are unused to date for manufacturing soft robots. They discuss how novel tactile sensors for soft robots could be created to enable better interaction of the soft robot with the environment. In conclusion this article highlights how embodied intelligence in soft robots could be used to make soft robots think and to make systems that can compute, autonomously, from sensory inputs.

The lower threshold as a unifying principle between Code Biology and Biosemiotics

Whether we emphasize the notion of 'sign' or the notion of 'code', either way the main interest of biosemiotics and Code Biology is the same, and we argue that the idea of the lower threshold is what still unifies these two groups. Code Biology concentrates on the notion of code: living organisms are defined as code-users or code-makers, and so it may be called the 'lower coding threshold' in this case. The semiotic threshold on the other hand is a concept without a specific definition. There are many possible ways of understanding this term. In order to maintain the lower threshold as the unifying concept between Code Biology and biosemiotics, it is important to be very clear about where this threshold is located and how it is defined. We focus on establishing the lower semiotic threshold at protein biosynthesis, and we propose basing the semiotic understanding of the lowest life forms on the following criteria: arbitrariness, representation, repetition, historicity and self-replication. We also offer that this definition of the lower threshold need not include the notion of interpretation, in the hope that this newly specified common principle of the lower threshold be accepted as a way forward in the conversation between Code Biology and biosemiotics.

Health information technology and digital innovation for national learning health and care systems

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Triple Stiffness: A Bioinspired Strategy to Combine Load-Bearing, Durability, and Impact-Resistance

Structures with variable stiffness have received increasing attention in the fields of robotics, aerospace, structural, and biomedical engineering. This is because they not only adapt to applied loads, but can also combine mutually exclusive properties. Here inspired by insect wings, the concept of "triple stiffness" is introduced and applied to engineering systems that exhibit three distinct deformability regimes. By implementing "flexible joints," "mechanical stoppers," and "buckling zones," structures are engineered to be not only load-bearing and durable, but also impact-resistant. To practice the performance of the design concept in real-life applications, the developed structures are integrated into 3D printed airplane wing models that withstood collisions without failure. The concept developed here opens new avenues for the development of structural elements that are load-bearing, durable, and impact-resistant at the same time.

Factors that influence parents' intentions of using autonomous vehicles to transport children to and from school

High-level autonomous vehicles (AVs) are likely to improve the quality of children's travel to and from school (such as improve travel safety and increase travel mobility). These expected benefits will not be presented if parents are not willing to use AVs. Therefore, it is necessary to explore parents' intentions of using AVs to transport children to and from school (parents' intentions). This study has two primary aims: 1) Exploring parents' intentions and their potential determinants. 2) Making recommendations for manufacturers to develop and market AVs suitable for school travel based on the research results. Research results show that face consciousness with Chinese characteristics can significantly affect parents' intentions. Knowledge of AVs is the most significant factor in influencing parents' intentions. Perceived usefulness, attitude towards school travel in AVs, and perceived risk can significantly affect parents' intentions. The direct impact of perceived ease of use and public engagement on parents' intentions is not significant. Finally, this research could provide decision-making support for governments and manufacturers to formulate relevant policies and marketing strategies, promoting parents' acceptance of AVs.

Visual behavior modelling for robotic theory of mind

Behavior modeling is an essential cognitive ability that underlies many aspects of human and animal social behavior (Watson in Psychol Rev 20:158, 1913), and an ability we would like to endow robots. Most studies of machine behavior modelling, however, rely on symbolic or selected parametric sensory inputs and built-in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition.

Automated technique for high-pressure water-based window cleaning and accompanying parametric study

The maintenance of buildings has become an important issue with the construction of many high-rise buildings in recent years. However, the cleaning of the outer walls of buildings is performed in highly hazardous environments over long periods, and many accidents occur each year. Various robots are being studied and developed to reduce these incidents and to relieve workers from hazardous tasks. Herein, we propose a method of spraying high-pressure water using a pump and nozzle, which differs from conventional methods. The cleaning performance parameters, such as water pressure, spray angle, and spray distance, were optimized using the Taguchi method. Cleaning experiments were performed on window specimens that were contaminated artificially. The cleaning performance of the proposed method was evaluated using the image-evaluation method. The optimum condition was determined based on the results of a sensitive analysis performed on the image data. In addition, the reaction force due to high pressure and impact force on the specimens were investigated. These forces were not sufficient to affect the propeller thrust or cause damage to the building’s surface. We expect to perform field tests in the near future based on the output of this research.

Effects of walking distance over robot-assisted training on walking ability in chronic stroke patients

An understanding of the dose-response during training is important to identify the rehabilitation programs to obtain the improvement in chronic stroke patients. The purpose of this study was to determine whether distance-dose (distance walked across all sessions) during robot-assisted training affects the change of walking speed and distance in chronic stroke patients after intervention. Fifteen chronic stroke patients were enrolled in this study. The patients performed 8 gait training sessions using the Hybrid Assistive Limb (HAL) for 3 weeks. Gait speed, stride length, cadence, and 2-minute walk test (2MWT) were measured before and post-intervention. Total walking distance (distance walked across all sessions) in individual patients were also measured. Gait speed, stride length, cadence, and 2-minute walk test (2MWT) improved significantly after training. The average of walking distance for 8 sessions in individual patients was 3793.3 ± 2105.3 m. Moreover, the change of gait speed (r = 0.53) and 2MWT (r = 0.70) were positively correlated with the walking distance during 8 sessions. This study of finding demonstrated that greater total distance walked over all sessions of training using the HAL is directly associated with the better walking outcomes in patients with chronic stroke. Further researches with a larger number of patients and a control group are needed to quantify the study results more precisely.

Results and Lessons Learned on Robotic Assisted Kidney Transplantation

INTRODUCTION

Nowadays, minimally invasive surgery in kidney transplantation is a reality thanks to robotic assistance. In this paper, we describe our experience, how we developed the robotic assisted Kidney transplantation (RAKT) technique, and analyze our results. Material and Methods. This is a retrospective study of all RAKTs performed at our center between July 2015 and March 2020. We describe the donor selection, surgical technique, and analyze the surgical results and complications. A comparison between the first 20 cases and the following ones is performed.

RESULTS

During the aforementioned period, 82 living donor RAKTs were performed. The mean age was 47.4 ± 13.4 and 50 (61%) were male. Mean body mass index was 25 ± 4.7 and preemptive in 63.7% of cases. Right kidneys and multiple arteries were seen in 14.6% and 12.2%, respectively. Mean operative and rewarming time was 197 ± 42 and 47 ± 9.6 minutes, respectively. Five cases required conversion to open surgery because of abnormal kidney vascularization. Two patients required embolization for subcapsular and hypogastric artery bleeding without repercussion. Three kidneys were lost, two of them due to acute rejection and one because venous thrombosis. Late complications requiring surgery included one kidney artery stenosis, one ureteral stenosis, two lymphoceles, and three hernia repairs. We noticed a significant reduction in time between the first 20 cases and the following ones from 248.25 ± 38.1 to 189.75 ± 25.3 (p < 0.05). With a mean follow-up time of 1.8 years (SD 1.3), the mean creatinine was 1.52 (SD 0.7) and RAKT graft survival was 98%.

CONCLUSIONS

The robotic approach is an attractive, minimally invasive method for kidney transplantation, yielding good results. Further studies are needed to consider it a standard approach.

Microbial contamination and efficacy of disinfection procedures of companion robots in care homes

Background

Paro and other robot animals can improve wellbeing for older adults and people with dementia, through reducing depression, agitation and medication use. However, nursing and care staff we contacted expressed infection control concerns. Little related research has been published. We assessed (i) how microbiologically contaminated robot animals become during use by older people within a care home and (ii) efficacy of a cleaning procedure.

Methods

This study had two stages. In stage one we assessed microbial load on eight robot animals after interaction with four care home residents, and again following cleaning by a researcher. Robot animals provided a range of shell-types, including fur, soft plastic, and solid plastic. Stage two involved a similar process with two robot animals, but a care staff member conducted cleaning. The cleaning process involved spraying with anti-bacterial product, brushing fur-type shells, followed by vigorous top-to-tail cleaning with anti-bacterial wipes on all shell types. Two samples were taken from each of eight robots in stage one and two robots in stage two (20 samples total). Samples were collected using contact plate stamping and evaluated using aerobic colony count and identification (gram stain, colony morphology, coagulase agglutination). Colony counts were measured by colony forming units per square centimetre (CFU/cm²).

Results

Most robots acquired microbial loads well above an acceptable threshold of 2.5 CFU/cm² following use. The bacteria identified were micrococcus species, coagulase negative staphylococcus, diptheriods, aerobic spore bearers, and staphylococcus aureus, all of which carry risk for human health. For all devices the CFU/cm² reduced to well within accepted limits following cleaning by both researcher and care staff member.

Conclusions

Companion robots will acquire significant levels of bacteria during normal use. The simple cleaning procedure detailed in this study reduced microbial load to acceptable levels in controlled experiments. Further work is needed in the field and to check the impact on the transmission of viruses.

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Monitoring patients through robotics telehealth systems is an interesting scenario where patients' conditions, and their environment, are dynamic and unknown variables. We propose to improve telehealth systems' features to include the ability to serve patients with their needs, operating as human caregivers. The objective is to support the independent living of patients at home without losing the opportunity to monitor their health status. Application scenarios are several, and they spread from simple clinical assisting scenarios to an emergency one. For instance, in the case of a nursing home, the system would support in continuously monitoring the elderly patients. In contrast, in the case of an epidemic diffusion, such as COVID-19 pandemic, the system may help in all the early triage phases, significantly reducing the risk of contagion. However, the system has to let medical assistants perform actions remotely such as changing therapies or interacting with patients that need support. The paper proposes and describes a multi-agent architecture for intelligent medical care. We propose to use the beliefs-desires-intentions agent architecture, part of it is devised to be deployed in a robot. The result is an intelligent system that may allow robots the ability to select the most useful plan for unhandled situations and to communicate the choice to the physician for his validation and permission.

Expressing uncertainty in Human-Robot interaction

Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed that even the use of numerical values to express probabilities does not guarantee an accurate understanding by laypeople. We therefore investigate if words can be used to communicate probability, such as "likely" and "almost certainly not". We embedded these phrases in the context of the usage of autonomous vehicles. The results show that the association of phrases to percentages is not random and there is a preferred order of phrases. The association is, however, not as consistent as hoped for. Hence, it would be advisable to complement the use of words with numerical expression of uncertainty. This study provides an empirically verified list of probabilities phrases that HRI researchers can use to complement the numerical values.

The future of upper extremity rehabilitation robotics: research and practice

The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain-machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain-machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.

Developing a novel force forecasting technique for early prediction of critical events in robotics

Safety critical events in robotic applications can often be characterized by forces between the robot end-effector and the environment. One application in which safe interaction between the robot and environment is critical is in the area of medical robots. In this paper, we propose a novel Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) technique to predict future values of any time-series sensor data, such as interaction forces. Existing time series forecasting methods have high computational times which motivates the development of a novel technique. Using Autoregressive Integrated Moving Average (ARIMA) forecasting as benchmark, the performance of the proposed model was evaluated in terms of accuracy, computation efficiency, and stability on various force profiles. The proposed algorithm was 11% more accurate than ARIMA and maximum computation time of CFDL-MFP was 4ms, compared to ARIMA (7390ms). Furthermore, we evaluate the model in the special case of predicting needle buckling events, before they occur, by using only axial force and needle-tip position data. The model was evaluated experimentally for robustness with steerable needle insertions into different tissues including gelatin and biological tissue. For a needle insertion velocity of 2.5mm/s, the proposed algorithm was able to predict needle buckling 2.03s sooner than human detections. In biological tissue, no false positive or false negative buckling detections occurred and the rates were low in artificial tissue. The proposed forecasting model can be used to ensure safe robot interactions with delicate environments by predicting adverse force-based events before they occur.

Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in Evolutionary Robotics

In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controller can often be near impossible, due to the high dimension and nonlinearities in the system. In this paper, we propose a more restricted form of controller, such that the underlying dynamical systems are forced to contain a dynamical object called a heteroclinic network. Systems containing heteroclinic networks share some properties with finite-state machines (FSMs) but are not discrete: both space and time are still described with continuous variables. Thus, we suggest that the heteroclinic networks can provide a hybrid between continuous and discrete systems. We investigate this innovated architecture in a minimal categorical perception task. The similarity of the controller to an FSM allows us to describe some of the system's behaviors as transition between states. However, other, essential behavior involves subtle ongoing interaction between the controller and the environment that eludes description at this level.

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge-the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.

Regenerative robotics

Congenital diseases requiring reconstruction of parts of the gastrointestinal tract, skin, or bone are a challenge to alleviate especially in rapidly growing children. Novel technologies may be the answer. This article presents the state-of-art in regenerative robotic technologies, which are technologies that assist tissues and organs to regenerate using sensing and mechanotherapeutical capabilities. It addresses the challenges in the development of such technologies, among which are autonomy and fault-tolerance for long-term therapy as well as morphological conformations and compliance of such devices to adapt to gradual changes of the tissues in vivo. The potential as medical devices for delivering therapies for tissue growth and as tools for scientific exploration of regenerative mechanisms is also discussed.

[Theoretical Review of Robot Anxiety]

The aim of the study is to describe the effect of robot anxiety. The forming of the concept and the description of similar constructs, like technological anxiety and computer anxiety, is presented through a historical review. Given our current knowledge of this topic it seems that cognitive aspects are the key factors underlying robot anxiety. According to literature dissemination of knowledge appears as the best way to lower anxiety. Attempts to develop cognitive therapies are also mentioned. As presented a widely accepted instrument for the measurement of robot anxiety is yet to be developed. RAS and NARS are introduced as possible scales as they are often adapted to studies although both leave things to be desired. The "Uncanny Valley" is also discussed, as it can explain how the appearance of robots affect the approval they gain from people, thus emphasising the importance of the design of robots.

Low-power microelectronics embedded in live jellyfish enhance propulsion

Artificial control of animal locomotion has the potential to simultaneously address longstanding challenges to actuation, control, and power requirements in soft robotics. Robotic manipulation of locomotion can also address previously inaccessible questions about organismal biology otherwise limited to observations of naturally occurring behaviors. Here, we present a biohybrid robot that uses onboard microelectronics to induce swimming in live jellyfish. Measurements demonstrate that propulsion can be substantially enhanced by driving body contractions at an optimal frequency range faster than natural behavior. Swimming speed can be enhanced nearly threefold, with only a twofold increase in metabolic expenditure of the animal and 10 mW of external power input to the microelectronics. Thus, this biohybrid robot uses 10 to 1000 times less external power per mass than other aquatic robots reported in literature. This capability can expand the performance envelope of biohybrid robots relative to natural animals for applications such as ocean monitoring.

Social Navigation in a Cognitive Architecture Using Dynamic Proxemic Zones

Robots have begun to populate the everyday environments of human beings. These social robots must perform their tasks without disturbing the people with whom they share their environment. This paper proposes a navigation algorithm for robots that is acceptable to people. Robots will detect the personal areas of humans, to carry out their tasks, generating navigation routes that have less impact on human activities. The main novelty of this work is that the robot will perceive the moods of people to adjust the size of proxemic areas. This work will contribute to making the presence of robots in human-populated environments more acceptable. As a result, we have integrated this approach into a cognitive architecture designed to perform tasks in human-populated environments. The paper provides quantitative experimental results in two scenarios: controlled, including social navigation metrics in comparison with a traditional navigation method, and non-controlled, in robotic competitions where different studies of social robotics are measured.

Mean-field models in swarm robotics: a survey

We present a survey on the application of fluid approximations, in the form of mean-field models, to the design of control strategies in swarm robotics. Mean-field models that consist of ordinary differential equations, partial differential equations, and difference equations have been used in the swarm robotics literature, depending on whether the state of each agent and the time variable take values from a discrete or continuous set. These macroscopic models are independent of the number of agents in the swarm, and hence can be used to synthesize robot control strategies in a scalable manner, in contrast to individual-based microscopic models of swarms that represent finite numbers of discrete agents. Moreover, mean-field models are amenable to rigorous investigation using tools from dynamical systems theory, control theory, stochastic processes, and analysis of partial differential equations, enabling new insights and provable guarantees on the dynamics of collective behaviors. In this paper, we survey the applications of these models to problems in swarm robotics that include coverage, task allocation, self-assembly, consensus, and environmental mapping.

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

Emergent coordination and situated learning in a Hybrid OR: The mixed blessing of using radiation

Mobilising knowledge and coordinating actions in order to make use of new innovations and technologies is a major challenge in the health care sector. Drawing upon a longitudinal, qualitative study of a Hybrid Operating Room in Sweden, we illustrate how the staff from a variety of medical specialties need to coordinate their tasks and competencies, and learn how to use the technology in a safe way. This study shows that learning across highly-professionalized communities is a recursive process of emergent coordination and situated learning, which includes the acknowledgement of others' expertise, task interdependence, and the pragmatic accommodation of latitude and control. Moreover, there was continuous negotiations between the different communities about what should constitute approved practice based on the task being performed. This obstructed the development of a dominant community with the authority to independently exclude other communities. We thus conclude that emergent coordination of tasks and expertise is an important aspect of learning how to use technologies that break with conventions of established and previously separated practices.

[Horizon of artificial intelligence and neurosciences. About robots, androids and cyborgs]

Artificial intelligence permits cerebral processes to be analyzed like computing processes. We can recognize two disturbing lines we can call: The Robot Project, android when is anthropomorphic, and The Cyborg Project. Robots are destined to perform repetitive, risky or accurate tasks in which they can surpass human limitations. No ethical conflicts are perceived here but there are new challenges to be faced as far as the social organization is concerned. As regards androids, apart from their robotic capabilities, their effect on the human being during interaction should be considered, as the impact of mimic's android on the emotion. The cyborgs are creatures composed by biological and cybernetic elements whose goal is to improve the capabilities of their biological parts. There has been no evidence of conflict in their use for rehabilitation or to supply impaired or non-existing functions. It would be different if they were used for manipulative activities. Another application of the cyborg project to consider is the term "enhancement", used to describe the increase of neurocognitive or sensory faculties through transcranial/intracranial stimulation. The ethical conflict here lies in the fact that the focus is not so much on healing but on seeking perfectibility or new modalities of perception. Health professionals must act in a new and constantly changing environment that transcends neurosciences and public health. Progress never stops; so, society have to be informed, anticipate dilemmas, and make room for reflection to help decision-making processes that involve individuals as well as the whole human species.

New discretization method applied to NBV problem: Semioctree

This paper presents a discretization methodology applied to the NBV (Next Best View) problem, which consists of determining the heuristical best position of the next scan. This new methodology is a hybrid process between a homogenous voxelization and an octree structure that preserves the advantages of both methods. An octree structure is not directly applicable to the NBV problem: as the point cloud grows with every successive scanning, the limits and position of the discretization, octree structure must coincide, in order to transfer the information from one scan to the next. This problem is solved by applying a first coarse voxelization, followed by the division of each voxel in an octree structure. In addition, a previous methodology for solving the NBV problem has been adapted to make use of this novel approach. Results show that the new method is three times faster than the homogenous voxelization for a maximum resolution of 0.2m. For this target resolution of 0.2m, the number of voxels/octants in the discretization is reduced approximately by a 400%, from 35.360 to 8.937 for the study case presented.

The robot nurses are coming to a workplace near you

As robot technology rapidly encroaches into most areas of our lives, second-year Kingston and St George's universities nursing student Jack Sherry remains optimistic that he will still have a job, jsherry38@gmail.com.

Robotic Rehabilitation and Spinal Cord Injury: a Narrative Review

Mobility after spinal cord injury (SCI) is among the top goals of recovery and improvement in quality of life. Those with tetraplegia rank hand function as the most important area of recovery in their lives, and those with paraplegia, walking. Without hand function, emphasis in rehabilitation is placed on accessing one's environment through technology. However, there is still much reliance on caretakers for many activities of daily living. For those with paraplegia, if incomplete, orthoses exist to augment walking function, but they require a significant amount of baseline strength and significant energy expenditure to use. Options for those with motor complete paraplegia have traditionally been limited to the wheelchair. While wheelchairs provide a modified level of independence, wheelchair users continue to face difficulties in access and mobility. In the past decade, research in SCI rehabilitation has expanded to include external motorized or robotic devices that initiate or augment movement. These robotic devices are used with 2 goals: to enhance recovery through repetitive, functional movement and increased neural plasticity and to act as a mobility aid beyond orthoses and wheelchairs. In addition, lower extremity exoskeletons have been shown to provide benefits to the secondary medical conditions after SCI such as pain, spasticity, decreased bone density, and neurogenic bowel. In this review, we discuss advances in robot-guided rehabilitation after SCI for the upper and lower extremities, as well as potential adjuncts to robotics.

Robotics in urology

Twenty years after it was introduced, robotic surgery has become more commonplace in urology – we examine its current uses and controversies

Robotics in neurosurgery

One of the first surgical specialties to adopt robotic procedures and one that continues to innovate

Robotics in trauma and orthopaedics

Recent advances and review of literature

Innovating With Rehabilitation Technology in the Real World: Promises, Potentials, and Perspectives

In this article, we discuss robotic-assisted therapy as an emerging and significant field of clinical rehabilitation and its value proposition for innovating rehabilitation clinical practice. Attempts to achieve integration among clinicians' practices and bioengineers' machines often generate new challenges and controversies. To date, the literature is indicative of a sizeable number and variety of robotic devices in the field of clinical rehabilitation, some are commercially available; however, large-scale clinical outcomes are less positive than expected. The following main themes related to integrating rehabilitation technology in real-world clinical practice will be discussed: the application of current evidence-based practice and knowledge in relation to treatment in the rehabilitation clinic, perspectives from rehabilitation professionals using robotic-aided therapy with regard to challenges, and strategies for problem solving. Lastly, we present innovation philosophies with regard to sustainability of clinical rehabilitation technologies.

Living with the animals: animal or robotic companions for the elderly in smart homes?

Although the use of pet robots in senior living facilities and day-care centres, particularly for individuals suffering from dementia, has been intensively researched, the question of introducing pet robots into domestic settings has been relatively neglected. Ambient assisted living (AAL) offers many interface opportunities for integrating motorised companions. There are diverse medical reasons, as well as arguments from animal ethics, that support the use of pet robots in contrast to living with live animals. However, as this paper makes clear, we should not lose sight of the option of living with animals at home for as long as possible and in conformity with the welfare of the animal assisted by AAL technology.

Progress in EEG-Based Brain Robot Interaction Systems

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

Artificial intelligence in medicine

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

When Should We Use Care Robots? The Nature-of-Activities Approach

When should we use care robots? In this paper we endorse the shift from a simple normative approach to care robots ethics to a complex one: we think that one main task of a care robot ethics is that of analysing the different ways in which different care robots may affect the different values at stake in different care practices. We start filling a gap in the literature by showing how the philosophical analysis of the nature of healthcare activities can contribute to (care) robot ethics. We rely on the nature-of-activities approach recently proposed in the debate on human enhancement, and we apply it to the ethics of care robots. The nature-of-activities approach will help us to understand why certain practice-oriented activities in healthcare should arguably be left to humans, but certain (predominantly) goal-directed activities in healthcare can be fulfilled (sometimes even more ethically) with the assistance of a robot. In relation to the latter, we aim to show that even though all healthcare activities can be considered as practice-oriented, when we understand the activity in terms of different legitimate 'fine-grained' descriptions, the same activities or at least certain components of them can be seen as clearly goal-directed. Insofar as it allows us to ethically assess specific functionalities of specific robots to be deployed in well-defined circumstances, we hold the nature-of-activities approach to be particularly helpful also from a design perspective, i.e. to realize the Value Sensitive Design approach.

Laparoscopic pancreatoduodenectomy: current status and future directions

In recent years, laparoscopic pancreatoduodenectomy (LPD) has been gaining a favorable position in the field of pancreatic surgery. However, its role still remains unclear. This review investigates the current status of LPD in high-volume centers. A literature search was conducted in PubMed, and only papers written in English containing more than 30 cases of LPD were selected. Papers with "hybrid" or robotic technique were not included in the analysis. Out of a total of 728 LPD publications, 7 publications matched the review criteria. The total number of patients analyzed was 516, and the largest series included 130 patients. Four of these studies come from the United States, 1 from France, 1 from South Korea, and 1 from India. In 6 reports, LPDs were performed only for malignant disease. The overall pancreatic fistula rate grades B-C were 12.7%. The overall conversion rate was 6.9%. LPD seems to be a valid alternative to the standard open approach with similar technical and oncological results. However, the lack of many large series, multi-institutional data, and randomized trials does not allow the clarification of the exact role of LPD.

New Frontiers for Psychology and Education: Robotics

The paper reviews the first attempts to study the educational and psychological usefulness of robotics: (1) the social and cooperative dimensions involved in the robot-building activities; (2) the reasoning strategies implied in building and programming robots; (3) the influences of robotics on mathematical and scientific achievement; (4) the use of robotics in modification of social skills of autistic children.

Robot chores: machines make the decisions on mock maternity ward

Robots could be used to make decisions on wards, American scientists have claimed.

[Rehabilitation and nursing-care robots]

In the extremely aged society, rehabilitation staff will be required to provide ample rehabilitation training for more stroke patients and more aged people with disabilities despite limitations in human resources. A nursing-care robot is one potential solution from the standpoint of rehabilitation. The nursing-care robot is defined as a robot which assists aged people and persons with disabilities in daily life and social life activities. The nursing-care robot consists of an independent support robot, caregiver support robot, and life support robot. Although many nursing-care robots have been developed, the most appropriate robot must be selected according to its features and the needs of patients and caregivers in the field of nursing-care.

[RESEARCH PROGRESS OF PERIPHERAL NERVE SURGERY ASSISTED BY Da Vinci ROBOTIC SYSTEM]

OBJECTIVE

To summarize the research progress of peripheral nerve surgery assisted by Da Vinci robotic system.

METHODS

The recent domestic and international articles about peripheral nerve surgery assisted by Da Vinci robotic system were reviewed and summarized.

RESULTS

Compared with conventional microsurgery, peripheral nerve surgery assisted by Da Vinci robotic system has distinctive advantages, such as elimination of physiological tremors and three-dimensional high-resolution vision. It is possible to perform robot assisted limb nerve surgery using either the traditional brachial plexus approach or the mini-invasive approach.

CONCLUSION

The development of Da Vinci robotic system has revealed new perspectives in peripheral nerve surgery. But it has still been at the initial stage, more basic and clinical researches are still needed.