RES URBIS aims at making it possible to convert several types of urban bio-waste into valuable bio-based products, in an integrated single biowaste biorefinery and by using one main technology chain. This goal will be pursued through: - collection and analysis of data on urban bio-waste production and present management systems in four territorial clusters that have been selected in different countries and have different characteristics. - well-targeted experimental activity to solve a number of open technical issues (both process- and product-related), by using the appropriate combination of innovative and catalogue-proven technologies. - market analysis whitin several economic scenarios and business models for full exploitation of bio-based products (including a path forward to fill regulatory gaps). Urban bio-waste include the organic fraction of municipal solid waste (from households, restaurants, caterers and retail premises), excess sludge from urban wastewater treatment, garden and parks waste, selected waste from food-processing (if better recycling options in the food chain are not available), other selected waste streams, i.e. baby nappies. Bio-based products include polyhydroxyalkanoate (PHA) and related PHA-based bioplastics as well as ancillary productions: biosolvents (to be used in PHA extraction) and fibers (to be used for PHA biocomposites). Territorial and economic analyses will be done either considering the ex-novo implementation of the biowaste biorefinery or its integration into existing wastewater treatment or anaerobic digestion plants, with reference to clusters and for different production size. The economic analysis will be based on a portfolio of PHA-based bioplastics, which will be produced at pilot scale and tested for applications: - Biodegradable commodity film - Packaging interlayer film - Speciality durables (such as electronics) - Premium slow C-release material for ground water remediation

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. On the other hand, robot manipulation is central to achieve the promise of robotics, since the definition of robot requires that it has actuators that it can use to change the world. In the last decades, a substantial growth has been observed in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create machines capable of directly and autonomously interacting with the world to implement useful applications. Learning will be central to such autonomous systems, as the real world contains too many variations for a robot to have an accurate model of human requests and behaviour, of the surrounding environment, the objects in it, or the skills required to manipulate them, in advance. The main objective of the IntelliMan project is focusing on the question of “How a robot can efficiently learn to manipulate in a purposeful and highly performant way”. IntelliMan will range from learning individual manipulation skills from human demonstration, to learning abstract descriptions of a manipulation task suitable for high-level planning, to discovering an object’s functionality by interacting with it, to guarantee performance and safety. IntelliMan aims at developing a novel AI-Powered Manipulation System with persistent learning capabilities, able to perceive the main characteristics and features of its surrounding by means of a heterogeneous set of sensors, able to decide how to execute a task in an autonomous way and able to detect failures in the task execution in order to request new knowledge through the interaction with humans and the environment. IntelliMan further investigates how such AI-powered manipulation systems are perceived by the users and what factors enhance human acceptability.

Collaborative robotics has established itself as a major force in pushing forward highly adaptive and flexible production paradigms in European large and small-medium enterprises. It is contributing to the sustainability and enhancement of Europe’s efficient and competitive manufacturing, to reshoring production, and to economic growth. However, still today the potential of collaborative technologies is largely underexploited. Indeed, collaborative robots are most often designed to coexist and to safely share a working space with humans. They are rarely thought to enter in direct socio-physical contact with humans to perceive, understand, and react to their distress or needs, and to enable them to work more productively and efficiently through better ergonomics. SOPHIA responds to this need by developing a new generation of socially cooperative human-robot systems in agile production. Its modular core technologies will enable dynamic state monitoring of the human-robot pair and anticipatory robot behaviours to: (1) improve human ergonomics, trust in automation, and productivity in manufacturing environments, and (2) achieve a reconfigurable, flexible, and resource-efficient production. By advancing the decisional autonomy and interaction ability of its innovative collaborative systems, SOPHIA will contribute to the reduction of work-related musculoskeletal disorders, the single largest category of work-related injuries and responsible for 30% of all workers’ compensation costs. SOPHIA’s societal relevance and the research groups’ experience in acceptability and standardization aspects of its core technologies will ensure their comfort-of-use by industrial workers, and the underlying design compliance to standards, thus strengthening the competitiveness in European manufacturing. We will illustrate and verify SOPHIA usability through the exploration of three real-world use-cases encouraging potential customers to integrate our core technologies in their workflow.