Colorectal cancer (CRC) represents a significant proportion of malignant diseases. Interventions are often carried out during the latter stages of development, leading to low patient survival rates and poor quality of life. In 2022 a European Commission report stated that “colonoscopy-based screening has higher sensitivity than testing for blood in stool, but it is less acceptable to participants”. At the same time, effective methods to treat polyps in the colon are limited. Current approaches are often associated with unsafe oncological margins and high complication rates, requiring life-changing surgery. EndoTheranostics will usher in a new era for screening colonoscopy, advancing the frontiers of medical imaging and robotics. A tip-growing or eversion robot with a sleeve-like structure will be created to extend deep into hollow spaces while perceiving the environment through multimodal imaging and sensing. It will also act as a conduit to transfer miniaturised instruments to the remote site within the colon for diagnosis and therapy (theranostics). With these capabilities, the system will be able to offer: 1. painless colon cleansing in preparation for endoscopy; 2. real-time polyp detection and tissue characterisation through AI-assisted multimodal imaging; 3. effective removal of polyps by conveying a “miniature mobile operating chamber” equipped with microsurgical tools to the target through the lumen of the eversion robot. The unique technical and clinical challenges will be tackled by the PIs, each bringing complementary skills, backed by their institutions with wide expertise and exceptional facilities. The synergy and added value evident in this team will lead to breakthroughs not possible through independent research. The outcomes of EndoTheranostics will revolutionise the theranostics of CRC, impacting the quality of life of millions of individuals. Ultimately it will launch a new era for endoluminal intervention with applications beyond medicine.

Artificial neural networks represent a key component of neuro-inspired computing for non-Boolean computational tasks. They emulate the brain by using nonlinear elements acting as neurons that are interconnected through artificial synapses. However, such physical implementations face two major challenges. First, interconnectivity is often constrained because of limits in lithography techniques and circuit architecture design; connections are limited to 100s, compared with 10000s in the human brain. Second, changing the weight of these individual interconnects dynamically requires additional memory elements attached to these links. Here, we propose an innovative architecture to circumvent these issues. It is based on the idea that dynamical hyperconnectivity can be implemented not in real space but in reciprocal or k-space. To demonstrate this novel approach we have selected ferromagnetic nanostructures in which populations of spin waves – the elementary excitations – play the role of neurons. The key feature of magnetization dynamics is its strong nonlinearity, which, when coupled with external stimuli like applied fields and currents, translates into two useful features: (i) nonlinear interactions through exchange and dipole-dipole interactions couple potentially all spin wave modes together, thereby creating high connectivity; (ii) the strength of the coupling depends on the population of each k mode, thereby allowing for synaptic weights to be modified dynamically. The breakthrough concept here is that real-space interconnections are not necessary to achieve hyper-connectivity or reconfigurable synaptic weights. The final goal is to provide a proof-of-concept of a k-space neural network based on interacting spin waves in low-loss materials such as yttrium iron garnet (YIG). The relevant spin wave eigenmodes are in the GHz range and can be accessed by microwave fields and spin-orbit torques to achieve k-space Neural computation with magnEtic exciTations.

While online grocery stores are expanding, supermarkets continue to provide customers with the sensory experience of choosing goods while walking between display shelves. Therefore, retail and logistics companies are concerned with making the shopping experience more comfortable and exciting while, at the same time, using technology to reduce costs and improve efficiency. The REFILLS project aims at developing robotic systems able to address the in-store logistics needs of the retail market. Three scenarios building on top of each other are considered. In the 1st scenario, mobile robots inspect shelves and generate semantic environment maps for layout identification and store monitoring. The 2nd scenario employs robot arms for autonomous sorting of articles in the backroom and for assisting human clerks with shelf refilling in the shop. In the 3rd scenario, the autonomy of the robot is strengthened, resulting in a robotic clerk capable of manipulating articles varying in shape, surface, fragility, stiffness and weight, and refill shelves without human intervention. These scenarios trigger a number of research and technology challenges that are tackled within REFILLS. Information on the supermarket articles is exploited to create powerful knowledge bases, which are used by the robots to identify shelves, recognize missing or misplaced articles, handling them and navigate the shop. Reasoning allows robots to cope with changing task requirements and contexts, and perception-guided reactive control makes them robust to execution errors and uncertainty. A modular approach is adopted for the design of cost-efficient robotic units. The work plan will generate exploitable results through three integration and evaluation phases. A final demonstration will take place at a real retail store. In sum, REFILLS is committed to generating wide impact in the retail market domain and beyond through the development of efficient logistics solutions for professional use in supermarkets.

HYFLIERS will develop two prototypes for the first worldwide hybrid aerial/ground robot with a hyper-redundant lightweight robotic articulated arm equipped with an inspection sensor, together with supporting services for efficient and safe inspection in industrial sites. Energy savings will be achieved by minimizing the time of flight and by performing the inspection while attached to the pipe. To ensure accurate positioning, guidance, landing and rolling on constrained surfaces such as pipes, the robot will rely on a control system also integrating environment perception, particularly for landing on the pipes, and aerodynamic control taking into account aerodynamic effects of the pipes. The system will also have multi-media interfaces for teleoperation, automatic collision detection and avoidance; a trajectory planning system that will take into account aerodynamic effects in addition to kinematic and dynamic models; and a mission planning system to optimize the use of the robot in the inspection. The technology results will be validated in the inspection of pipes, which is a very relevant short-term application. HYFLIERS will decrease the cost and risks of current human inspection in production plants, such as oil and gas, where it is estimated that about 50 000 pipe thickness measurement points are needed within a 3 to 5 years interval. HYFLIERS will eliminate the risks of accidental falls and the cost associated to the use of man-lifts, cranes, scaffold or rope access, which is many orders of magnitude larger than the measurement cost by itself. Taking into account that about 60% to 75% of inspection costs in this type of facilities is dedicated to ultrasonic thickness measurements, the project will concentrate on these measurements. The results of the project could be also applied to other industrial scenarios, such as power generation plants.