CERES aims at evaluating possible commercial applications of new protein-based down-converting red-emitting LED sources. Laboratory prototypes exhibit an extraordinary stability and efficiency as well as an easy-to-tune emission spectrum. This contrasts with the commercial low-energy emitting LED technology. CERES will focus on a validation phase including i) the optimization of the upscaling of protein production, the preparation of large-area protein-polymer color filters, and the assembly of red-emitting LED arrays and ii) the test in pre-industrial crop controlled environment agriculture growing boxes. This information will be paramount to realize a realistic market impact.

One of the main challenges of roboticists is to take robots out of the factories and let them enter into unstructured environments, such as houses, hospitals, small manufacturers and dangerous area. The objective of the project is to take a step towards the presence of robots in such environments. Currently, there are still important obstacles to the massive diffusion of advanced mobile manipulation systems in the fields described above. First of all, programming mobile manipulators with the classical methods is still too expensive and time-consuming due to intrinsic complexity of mobile manipulation tasks. A second limitation is that planning the robot motion completely off-line, as often happens in classical industrial applications, may likely bring to a failure of the assigned task, since a high degree of uncertainty is present and the environment can dynamically change. Such features may cause safety issues for humans potentially present in the workspace and for the external environment itself. In order to tackle these limitations, the LEACON project has the objective to develop a framework that: - allows robots to learn in a real world scenario manipulation skills from human demonstration -exploits multimodal perception (tactile, proximity, visual, force sensors) to increase the robustness to unforeseen events and safety when manipulation tasks are executed. To fulfill such objectives, a multidisciplinary approach that combines machine learning and perception-based control is proposed. The core of the proposed framework will provide two planning levels tightly connected: the high-level and low-level cognitive system. To show the effectiveness of the developed architecture, the main use cases will be constituted by a robot that performs picking, manipulation, and placing operations in a dynamic, unstructured environment in presence of humans in its workspace. At the end of the project, the developed software will be released as open source code.

Parameterized systems consist of an arbitrary number of replicated agents with limited computational power, interacting to achieve common goals. They pervade computer science. Classical examples include families of digital circuits, distributed algorithms for leader election or byzantine agreement, routing algorithms, and multithreaded programs. Modern examples exhibit stochastic interaction between mobile agents, and include robot swarms, molecular computers, and cooperating ant colonies. A parameterized system is in fact an infinite collection of systems, one for each number of agents. Current verification technology of industrial strength can only check correctness of a few instances of this collection. For example, model checkers can automatically prove a distributed algorithm correct for a small number of processes, but not for any number. While substantial progress has been made on the theory and applications of parameterized verification, in order to achieve large impact the field has to face three ``grand challenges'': - Develop novel algorithms and tools for p-verification of classical p-systems that bypass the high complexity of current techniques. -Develop the first algorithms and tools for p-verification of modern stochastic p-systems. -Develop the first algorithms and tools for synthesis of correct-by-construction p-systems. Addressing these challenges requires fundamentally new lines of attack. The starting point of PaVeS are two recent breakthroughs in the theory of Petri nets and Vector Addition Systems, one of them achieved by the PI and his co-authors. PaVeS will develop these lines into theory, algorithms, and tools for p-verification and p-synthesis, leading to a new generation of verifiers and synthesizers.