TORNADO will develop an innovative, multifunctional and adaptive cloud robotics platform, supporting advanced navigation of an autonomous mobile robot (AMR) within complex, time-varying, real-world, human-populated indoor environments. The TORNADO AMR will be able to manipulate small, soft or deformable objects (SSDs) to an unprecedented degree of success, as well as to naturally interact with humans via hand gestures or verbal conversation, by exploiting the zero-shot generalization abilities of deep neural Foundation Models (FMs) for robotics. The AMR's intelligence will rely on a pool of pretrained cloud-hosted FMs, which shall be further adjusted on-the-fly to the current situation via Out-of-Distribution Detection, Test-Time Adaptation and Few-Shot Adaptation subsystems. These will exploit human feedback if available, but will also support autonomous and dynamic cognitive adaptation. Additionally, the TORNADO system will be able to automatically select and set-up on-the-fly the most suitable combination of FMs and non-neural robotics algorithms during deployment, depending on the current situation. In cases of failure, on-the-fly skill acquisition will be supported via integrated, novel Learning-from-Demonstration methods facilitated by an innovative Augmented Reality (AR) interface and eXplainable AI (XAI) algorithms. The adaptive TORNADO system will allow the robot to perform difficult, non-repetitive manipulation tasks on previously unseen SSDs that may change shape during handling, as well as to flexibly adjust to SSDs of different sizes during operation. Measurement of human trust to interactive robots and human behavioral modeling will aid optimal integration/acceptance of TORNADO into society. Validation will take place at TRL-5 in 3 different industrial Use-Cases: flexible small gears manipulation and deformable ply-sheets handling (gears factory), palliative patient care (hospital) and product quality sampling/waste collection (dairy processing plant).

FOODGUARD aims to develop and demonstrate co-created solutions that will support innovations & advances based on microbiome, microbial activities & technology hubs to address food, health, economic and environmental challenges.The envisioned approach consists of a framework of toolsets & methodologies to provide sustainable solutions in food processing, packaging & across the food value chain to address food shelf-life increase & waste reduction in a holistic manner.The proposed solutions aim to (a) extend food shelf life with novel packaging/ biopreservation i.e. use of protective cultures/synthetic microbial consortia, recyclable films with natural antimicrobials or protective cultures;(b)monitor food quality/safety/shelf life with microbial indicators/molecular biomarkers used in smart packaging (TTIs, smart printed tags, no-invasive sensors)& (c) accurately predict food shelf life & improve traceability using predictive models, AI/ML, Internet of Things & tools like QR,NFR etc.; FOODGUARD toolbox components will be extensively evaluated in real life settings through four pilot demonstrations in 4 different countries with involvement of all relevant actors while covering diverse requirements and different food products. FOODGUARD outcomes target i) to minimize food loss and waste by shelf life extension and prediction,ii) to help the food industry to implement these preservation solutions as an alternative to chemical preservatives,iii)to deploy responsive policy for implementing these approaches as well as to engage consumers educated via tools/platforms, effectively improving awareness &trust in the food sector to(iv)increase traceability, providing real-time supply transparency that will improve the uptake of data-driven innovations in food systems, optimize resource efficiency (reducing food waste from farm to fork)(v)manage increased complexity in agri-food production & supply chain process, make it easier for consumers to adopt a healthy & safe food diet.