Although the progress in the efficiency of residential buildings, the consumption does not decrease as expected. Solution for involving inhabitants in sobriety and flexibility have already been proposed but they rely on site knowledge models. However each site is unique because of its architecture, equipment and sensors but also because of its occupants. The site models are mostly not available. LearningHome aims at developing cooperative and interactive learning for home inhabitants to confront to an Interactive Home Energy Management Aid System (IHEMAS) to yield knowledge about occupant activities and costs/comforts preferred compromise. LearningHome extends the promising concepts opened up by the ANR INVOLVED project regarding interactions by developing cooperative solutions to learn a global human-system learnt representation. The aim is to identify the practices as well as the activities of the occupants by reconciling the perceptions of the IHEMAS and its more or less numerous sensors with the perceptions of the inhabitants. These perceptions will be translated into activity labels, intentions and preferences, taking into account the volatility of the inhabitants' memory and limited consent to interact with an IHEMAS. It induces learning methods with ad hoc notifications but also mechanisms for matching the inhabitants and IHEMAS perceptions. It might be discrepancies between the IHEMAS and inhabitants perceptions because of a little number of sensors or because of a too high complexity in the inhabitant perceptions. These confusions must be resolved by automatically adapting for instance the generated features. Combining sensor data with labels from occupants yield a model thanks to learning algorithms. Interactive learning is a complementary method to discover the energy behavior of a site. Contrary to the INVOLVED approach, explanations and advice are generated without an a priori physical model, but by exploiting similar encountered situations. The aim is to conceive an exploratory approach guiding the inhabitants in the discovery of the effects of actions in similar situations. Inhabitants will thus be put in situation of experimenters of their environment and the IHEMAS will have the role of recording the experiments and guiding inhabitants towards new exploratory. It will engage inhabitants of residential buildings towards sober energy management through user interaction and that helps them to maintain their behavior change over time. According to J. Grudin, the future of Human Computer Interaction (HCI) are smart digital partners. Thus, the goal is to investigate mixed initiative through symmetrical co-learning interactions: both parties will inform, explain, ask, suggest and learn from the other. It is a new paradigm for IIHS, which fits well the unicity of each home where knowledge raises up from confrontation of parties. Our hypothesis is that co-learning will leverage user engagement as it puts users back in the decision loop by letting them to control the system boundaries. LearningHome will experiment different approaches to involve occupants to be more sober and more flexible in collective residential buildings. Different behavioral levers are going to be tested. One challenge is about measuring the impact of each lever: while it is relatively easy to measure a lever impact on energy consumption, it is difficult to assess an impact on energy used for heating. The assessments of the results follow two complementary approaches: an energy performance verification protocol for measuring over a few weeks the energy impacts of levers by measuring then for extrapolating them to a year by propagating uncertainties, and the analysis of household behavior regarding energy usage.

Collaboration 4.0 project is a contribution to the main industry of the future challenge of the efficient place of humans in the factory of the future. Industry of the future means the high-tech digitalization of production systems to get more flexibility of the whole value chain to achieve personalized products and sustainability. Enabling technologies like Internet Of Things, wearables, robotics, Artificial Intelligence and 3D Printings are the key drivers of this industrial transformation. The main challenge is to keep the economic value of mass production (3.0) when competitive lot-size 1 personalized production (4.0). The Collaboration 4.0 project aims at studying working situations enabled by the new digital technologies in 4.0 industrial environment for their productivity and attractive features. The project addresses the Nb 3 ANR research axis “Fostering industrial renewal” and especially the Nb 1sub-axis “Factory of the future: Human, organization and technologies”. Its overarching objective is to design collaborative workplaces of the future in which workers and machines are closely combined to reach new sustainable performance in 4.0 industrial environment. The project is featured from three fundamental research hypotheses: 1) The Human-Machine collaborative activity of the future will be carried out in a new enabling competence-based industrial environment, 2) Digital technologies are flexible and frequently evolve, 3) Work and industrial organization highly influences the well performing Human-Machine collaborative activity. The project aims at designing new workplaces in which workers and machines share the same space to complete shared tasks by using work-enabling digital technologies. The worker will manage work activities controlling the machine tasks and instructing it. The machine is designed to meet the worker needs. It could provide worker with new ways of working. We want to define and characterize the new types of 4.0 collaborative workplaces useful and well performing in a specific industrial situation. The core issues are the efficient technology uses while producing and the industrial organization to be set up. Concretely, the project will study two different work situations from two case studies: a collaborative activity between a robot and a human on one side and between an augmented reality wearable and a human on the other side. Delivered results will be a classification of human-Machine collaborative work situations in an enabling industrial environment, a framework for analyzing an enabling collaborative industrial activity and recommendations for designing enabling industrial workplaces. The project is a multidisciplinary project combining industrial engineering, ergonomics and digital technologies. It is featured in five scientific tasks and one management task. A workplace-of-the-future demonstrator will be developed at the Grenoble INP S.MART technological platform from existing facilities. An industrial advisory board accompany the research partners to operationalize the theoretical propositions. It is a 48-month project and relies on two PhD thesis and an engineer position. The project will be managed by G-SCOP laboratory (industrial engineering, augmented reality) alongside with LIG (robotics and HMI) and ACTé (ergonomics). Each laboratory will bring to the project their human resources and equipment as necessary. Project results will be spread through scientific publications, guidelines for industrial companies and communication activities.

Induced subgraphs play a central role in both structural and algorithmic graph theory. A graph H is an induced subgraph of a graph G if one can delete vertices of G to obtain H. This is the strongest notion of subgraph, hence being H-free (that is not containing H as an induced subgraph) is not a very restrictive requirement. Weaker notions of containment, like for instance minors, are now well understood, and the next achievement in Graph Theory should certainly be the understanding of forbidden induced structures. We focus in this proposal on the following very general question: Given a (possibly infinite) family F of graphs, what properties does a F-free graph have? This is the key question of many important and longstanding problems, because many crucial graph classes are defined in terms of forbidden induced subgraphs. This field is now quickly growing, and new techniques and tools have been recently developed. Our first goal is to establish bounds on some classical graph parameters for F-free graphs, such as the clique number, the stability number and the chromatic number. A second goal is to design efficient algorithms to recognize F-free graphs and to determine or approximate some parameters for those graphs. We also plan to study similar questions for oriented graphs. For this purpose, we plan to use and develop various proof techniques, some of these being recently discovered, such as the structural description of graph classes, the regularity lemma, graph limits, flag algebras, VC-dimension, discharging method as well as computer-assisted proofs.

The project ArchiTOOL aims at inventing, prototyping, and evaluating an immersive and intelligent virtual environment for architecting complex technological systems. Instead of using domain-specific engineering software, the immersive and interactive environment will provide the architect with the modelling capabilities required to define the various views (operational, specification, functional, behavioural, structural, logic, safety, etc.) of a system architecture in a single virtual space before exporting each viewpoint in a standardised format that will enable domain-experts to continue with a detailed design. Moreover, the immersive environment will include a cognitive agent to support the system architect with intelligent capabilities: models verification, context-aware recommendation of rules, identification and automation of modelling routines...

The VIVAE project focuses on power electronics (PE) systems to increase their lifetime or to preserve the functional, environmental and economic high value of their subsystems with respect to industry constraints. These considerations often delayed or even not studied due to the conservatism of industrial actors, despite the high repair potential or preservation of residual values inherent in PE systems (components or materials based on the evaluation of different end-of-life scenarios). VIVAE will propose an integrated modular re-design method for the circular economy of these products until standards proposal. It will also propose method and indicators to evaluate the residual values of the system and its subparts and components, in order to assess the best repair / recovery scenarios. Ecodesign of these new generations of EPs is coupled with the development of a proof of concept of a robot-cobot dis-assembly cell interacting with an augmented operator.