The societal needs such as helping elderly and rapid technological advances have transformed robotics in recent years. Making robots autonomous and at the same time able to interact safely with real world objects is desired in order to extend their range of applications to highly interactive tasks such as caring for the elderly. However, attaining robots capable of doing such tasks is challenging as the environmental model they often use is incomplete, which underlines the importance of sensors to obtain information at a sufficient rate to deal with external change. In robotics, the sensing modality par excellence so far has been vision in its multiple forms, for example lasers, or simply stereoscopic arrangements of conventional cameras. On other hand the animal world uses a wider variety of sensory modalities. The tactile/touch sensing is particularly important as many of the interactive tasks involve physical contact which carry precious information that is exploited by biological brains and ought to be exploited by robots to ensure adaptive behaviour. However, the absence of suitable tactile skin technology makes this task difficult. PRINTSKIN will develop a robust ultra-flexible tactile skin and endow state-of-the-art robotic hand with the tactile skin and validate the skin by using tactile information from large areas of robot hands to handle daily object with different curvatures. The tactile skin will be benchmarked against available semi-rigid skins such as iCub skin from EU project ROBOSKIN and Hex-O-Skin. The skin will be validated on at least two different industrial robotic hands (Shadow Hand and i-Limb) that are used in dexterous manipulation and prosthetics. The robust ultra-thin tactile skin will be developed using an innovative methodology involving printing of high-mobility materials such as silicon on ultra-flexible substrates such as polyimide. The tactile skin will have solid-state sensors (touch, temperature) and electronics printed on ultra-flexible substrates such as polyimide. The silicon-nanowires based ultra-thin active-matrix electronics in the backplane will be covered with a replaceable soft transducer layer. Integration of electronic and sensing modules on a foil or as stack of foils will be explored. 'Truly bottom-up approach' is the distinguishing feature of PRINTSKIN methodology as the development of tactile skin will begin with atom by atom synthesis of nanowires and finish with the development of tactile skin system - much like the way nature uses proteins and macromolecules to construct complex biological systems. This new technological platform to print tactile skin will enable an entirely new generation of high-performance and cost-effective systems on flexible substrates. Fabrication by printing will have important implications for cost-effective integration over large areas and on nonconventional substrates, such as plastic or paper. Printing of high-performance electronics is also appealing for mask-less approach, reduced material wastage, and scalability to large area. The proposed programme thus has the potential to emulate yet another revolution in the electronics industry and trigger transformation in various sectors including, robotics, healthcare, and wearable electronics.

The nuclear industry has some of the most extreme environments in the world, with radiation levels and other hazards frequently restricting human access to facilities. Even when human entry is possible, the risks can be significant and very low levels of productivity. To date, robotic systems have had limited impact on the nuclear industry, but it is clear that they offer considerable opportunities for improved productivity and significantly reduced human risk. The nuclear industry has a vast array of highly complex and diverse challenges that span the entire industry: decommissioning and waste management, Plant Life Extension (PLEX), Nuclear New Build (NNB), small modular reactors (SMRs) and fusion. Whilst the challenges across the nuclear industry are varied, they share many similarities that relate to the extreme conditions that are present. Vitally these similarities also translate across into other environments, such as space, oil and gas and mining, all of which, for example, have challenges associated with radiation (high energy cosmic rays in space and the presence of naturally occurring radioactive materials (NORM) in mining and oil and gas). Major hazards associated with the nuclear industry include radiation; storage media (for example water, air, vacuum); lack of utilities (such as lighting, power or communications); restricted access; unstructured environments. These hazards mean that some challenges are currently intractable in the absence of solutions that will rely on future capabilities in Robotics and Artificial Intelligence (RAI). Reliable robotic systems are not just essential for future operations in the nuclear industry, but they also offer the potential to transform the industry globally. In decommissioning, robots will be required to characterise facilities (e.g. map dose rates, generate topographical maps and identify materials), inspect vessels and infrastructure, move, manipulate, cut, sort and segregate waste and assist operations staff. To support the life extension of existing nuclear power plants, robotic systems will be required to inspect and assess the integrity and condition of equipment and facilities and might even be used to implement urgent repairs in hard to reach areas of the plant. Similar systems will be required in NNB, fusion reactors and SMRs. Furthermore, it is essential that past mistakes in the design of nuclear facilities, which makes the deployment of robotic systems highly challenging, do not perpetuate into future builds. Even newly constructed facilities such as CERN, which now has many areas that are inaccessible to humans because of high radioactive dose rates, has been designed for human, rather than robotic intervention. Another major challenge that RAIN will grapple with is the use of digital technologies within the nuclear sector. Virtual and Augmented Reality, AI and machine learning have arrived but the nuclear sector is poorly positioned to understand and use these rapidly emerging technologies. RAIN will deliver the necessary step changes in fundamental robotics science and establish the pathways to impact that will enable the creation of a research and innovation ecosystem with the capability to lead the world in nuclear robotics. While our centre of gravity is around nuclear we have a keen focus on applications and exploitation in a much wider range of challenging environments.

Engineered systems are increasingly being used autonomously, making decisions and taking actions without human intervention. These Autonomous Systems are already being deployed in industrial sectors but in controlled scenarios (e.g. static automated production lines, fixed sensors). They start to get into difficulties when the task increases in complexity or the environment is uncontrolled (e.g. drones for offshore windfarm inspection), or where there is a high interaction with people and entities in the world (e.g. self-driving cars) or where they have to work as a team (e.g. cobots working in a factory). The EN-TRUST Vision is that these systems learn situations where trust is typically lost unnecessarily, adapting this prediction for specific people and contexts. Stakeholder trust will be managed through transparent interaction, increasing the confidence of the stakeholders to use the Autonomous Systems, meaning that they can be adopted in scenarios never before thought possible, such as doing the jobs that endanger humans (e.g. first responders or pandemic related tasks). The EN-TRUST 'Trust' Node will perform foundational research on how humans and Autonomous Systems (AS) can work together by building a shared reality, based on mutual understanding through trustworthy interaction. The EN-TRUST Node will create a UK research centre of excellence for trust that will inform the design of Autonomous Systems going forward, ensuring that they are widely used and accepted in a variety of applications. This cross-cutting multidisciplinary approach is grounded in Psychology and Cognitive Science and consists of three "pillars of trust": 1) computational models of human trust in AS; 2) adaptation of these models in the face of errors and uncontrolled environments; and 3) user validation and evaluation across a broad range of sectors in realistic scenarios. This EN-TRUST framework will explore how to best establish, maintain and repair trust by incorporating the subjective view of human trust towards Autonomous Systems, thus maximising their positive societal and economic benefits.

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

This Centre for Doctoral Training in Embedded Intelligence, the first in the UK, addresses high priority areas for economic growth such as autonomous complex manufactured products and systems, functional materials with high performance systems, data-to-knowledge solutions (e.g. digital healthcare and digitally connected citizens), and engineering for industry, life and health, which are also key priorities for Horizon 2020, the new EU framework programme for research and innovation. Horizon 2020 explicitly spells out ICT and Manufacturing as key industrial technologies. Its remit fits the EPSRC priority areas of ICT for Manufacturing and Data to Knowledge, and has an impact on industrial sectors as diverse as logistics, metrology, food, automotive, oil & gas, chemistry, or robotics. In addition, our world (homes, transport, workplaces, supplies of food, utilities, leisure or healthcare) is constantly seeking for interactive technologies and enhanced functionalities, and we will rely on these graduates who can translate technologies for the end-user. The uniqueness of this Centre resides on the capability to innovatively address a myriad of Embedded Intelligence challenges posed by technical needs ranging from the EI supply chain: the design stage, through manufacturing of embedded or on-bedded devices, to the software behind data collection, as well as integrative technologies, to finally the requirements from end-users. The thematic areas, discussed conjointly with industry during the preparation of this proposal, allow us also to recruit students from a vast range of educational backgrounds. A strong user pull defines the nature of the challenges that this CDT will tackle. The graduates who shall come to alleviate the shortage of skilled engineers and technologists in the field will be exposed to the following thematic areas: > Device design, specification of sensors and measurement devices (power scavenging, processing, wire & wireless communications, design for low power, condition monitoring); > Packaging & integration technologies (reliability and robustness, physical and soft integration of devices, sub-components and wider system environment); > Intelligent software (low level, embedded, system level, database integration, ontology interrogation, service oriented architectures, services design); > Manufacturing solutions (design for manufacture of embedded systems, advanced and hybrid manufacturing processes for embedding, process consolidation technologies, biomimetics and cradle-to-cradle for sustainability production, etc.); > Applications engineering (design and implementation of embedded technologies for in-time, in-line products, processes and supply chains; product and process design for embedded intelligence); > System Services: (i) Service Foundations (e.g., dynamically reconfigurable architectures, data and process integration and semantic enhanced service discovery); (ii) Service Composition (e.g. composability analyses, dynamic and adaptive processes, quality of service compositions, business driven compositions); (iii) Service Management and Monitoring (e.g. self: -configuring, -adapting, -healing, -optimising and -protecting) and (iv) Service Design and Development (e.g. engineering of business services, versioning and adaptivity, governance across supply chains). Our flagship, the 'Transition Zone' training, will facilitate the transition into doctoral studies in the first year of studies, and, closer to the end of the programme, out to industry or self-employment. As employable high calibre individuals with a good understanding of enterprising, commercialisation of research, social responsibility, gender equality and diversity, innovation management, workplaces, leadership and management, our doctorates will grow prosperity bottom up, enjoying a wealthy network of academic and industrial contacts from their years at the CDT, as well as their peers at the Centre.