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.

A decade retrospective of medical robotics research from 2010 to 2020.

Robotics is a forward-looking discipline. Attention is focused on identifying the next grand challenges. In an applied field such as medical robotics, however, it is important to plan the future based on a clear understanding of what the research community has recently accomplished and where this work stands with respect to clinical needs and commercialization. This Review article identifies and analyzes the eight key research themes in medical robotics over the past decade. These thematic areas were identified using search criteria that identified the most highly cited papers of the decade. Our goal for this Review article is to provide an accessible way for readers to quickly appreciate some of the most exciting accomplishments in medical robotics over the past decade; for this reason, we have focused only on a small number of seminal papers in each thematic area. We hope that this article serves to foster an entrepreneurial spirit in researchers to reduce the widening gap between research and translation.

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.

Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care.

The last decade (2010-2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic literature in eight different domains: (1) robotic dental assistants; (2) robotics in diagnosis and simulation of orthodontic problems; (3) robotics in orthodontic patient education, teaching, and training; (4) wire bending and customized appliance robotics; (5) nanorobots/microrobots for acceleration of tooth movement and for remote monitoring; (6) robotics in maxillofacial surgeries and implant placement; (7) automated aligner production robotics; and (8) TMD rehabilitative robotics. A total of 1,150 records were searched, of which 124 potentially relevant articles were retrieved in full. 87 studies met the selection criteria following screening and were included in the scoping review. The review found that studies pertaining to arch wire bending and customized appliance robots, simulative robots for diagnosis, and surgical robots have been important areas of research in the last decade (32%, 22%, and 16%). Rehabilitative robots and nanorobots are quite promising and have been considerably reported in the orthodontic literature (13%, 9%). On the other hand, assistive robots, automated aligner production robots, and patient robots need more scientific data to be gathered in the future (1%, 1%, and 6%). Technological readiness of different robotic applications in orthodontics was further assessed. The presented eight domains of robotic technologies were assigned to an estimated technological readiness level according to the information given in the publications. Wire bending robots, TMD robots, nanorobots, and aligner production robots have reached the highest levels of technological readiness: 9; diagnostic robots and patient robots reached level 7, whereas surgical robots and assistive robots reached lower levels of readiness: 4 and 3, respectively.

The rise of intelligent matter.

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.

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.

Perinatal stroke: mapping and modulating developmental plasticity.

Most cases of hemiparetic cerebral palsy are caused by perinatal stroke, resulting in lifelong disability for millions of people. However, our understanding of how the motor system develops following such early unilateral brain injury is increasing. Tools such as neuroimaging and brain stimulation are generating informed maps of the unique motor networks that emerge following perinatal stroke. As a focal injury of defined timing in an otherwise healthy brain, perinatal stroke represents an ideal human model of developmental plasticity. Here, we provide an introduction to perinatal stroke epidemiology and outcomes, before reviewing models of developmental plasticity after perinatal stroke. We then examine existing therapeutic approaches, including constraint, bimanual and other occupational therapies, and their potential synergy with non-invasive neurostimulation. We end by discussing the promise of exciting new therapies, including novel neurostimulation, brain-computer interfaces and robotics, all focused on improving outcomes after perinatal stroke.

Progress in robotics for combating infectious diseases.

The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.

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.

Reducing negative emotions in children using social robots: systematic review.

BACKGROUND: For many children, visiting the hospital can lead to a state of increased anxiety. Social robots are being explored as a possible tool to reduce anxiety and distress in children attending a clinical or hospital environment. Social robots are designed to communicate and interact through movement, music and speech. OBJECTIVE: This systematic review aims at assessing the current evidence on the types of social robots used and their impact on children's anxiety or distress levels when visiting the hospital for outpatient appointments or planned admissions. METHODS: Databases such as MEDLINE, PubMed, IEEE Xplore, Web of Science, PsychINFO and Google Scholar were queried for papers published between January 2009 and August 2020 reporting the use of social robots interacting with children in hospital or clinical environments. RESULTS: A total of 10 studies were located and included. Across these 10 studies, 7 different types of robots were used. Anxiety and distress were found to be reduced in the children who interacted with a social robot. CONCLUSIONS: Overall, the evidence suggests that social robots hold a promising role in reducing levels of anxiety or distress in children visiting the hospital. However, research on social robots is at an early stage and requires further studies to strengthen the evidence base.

Future perspectives of robot psychiatry: can communication robots assist psychiatric evaluation in the COVID-19 pandemic era?

PURPOSE OF REVIEW: Direct face-to-face interview between a psychiatrist and a patient is crucial in psychiatric evaluation, however, such traditional interviews are becoming difficult to conduct because of the infection risk in the COVID-19 era. As telepsychiatry, video interviews using internet are suggested to be useful to evaluate and assist individuals with mental disorders. However, some patients especially with social phobia, depression, and autism spectrum disorder (ASD) hesitate to use even such face-to-face-like tools. Communication robots have been proposed as future assistant tools for such patients. Herein, we summarize recent advancements in robot psychiatry, and propose the benefits of communication robots in psychiatric evaluation. RECENT FINDINGS: Recent studies have suggested that communication robots are effective in assisting people with ASD. As a pilot trial, we herein conducted semi-structured interviews to evaluate depression and hikikomori, a form of pathological social withdrawal, using a communication robot and a psychiatrist, respectively. There was almost identical evaluation between the two. Interestingly, a person with hikikomori answered that the robot was easier to disclose. SUMMARY: Robots can reduce the burden of human resources and the infection risk in the COVID-19 era. Robot interview is expected to be implemented for future evaluation system in psychiatry.

t-soft update of target network for deep reinforcement learning.

This paper proposes a new robust update rule of target network for deep reinforcement learning (DRL), to replace the conventional update rule, given as an exponential moving average. The target network is for smoothly generating the reference signals for a main network in DRL, thereby reducing learning variance. The problem with its conventional update rule is the fact that all the parameters are smoothly copied with the same speed from the main network, even when some of them are trying to update toward the wrong directions. This behavior increases the risk of generating the wrong reference signals. Although slowing down the overall update speed is a naive way to mitigate wrong updates, it would decrease learning speed. To robustly update the parameters while keeping learning speed, a t-soft update method, which is inspired by Student-t distribution, is derived with reference to the analogy between the exponential moving average and the normal distribution. Through the analysis of the derived t-soft update, we show that it takes over the properties of the Student-t distribution. Specifically, with a heavy-tailed property of the Student-t distribution, the t-soft update automatically excludes extreme updates that differ from past experiences. In addition, when the updates are similar to the past experiences, it can mitigate the learning delay by increasing the amount of updates. In PyBullet robotics simulations for DRL, an online actor-critic algorithm with the t-soft update outperformed the conventional methods in terms of the obtained return and/or its variance. From the training process by the t-soft update, we found that the t-soft update is globally consistent with the standard soft update, and the update rates are locally adjusted for acceleration or suppression.

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.