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INRIA

French Institute for Research in Computer Science and Automation
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941 Projects, page 1 of 189
  • Funder: European Commission Project Code: 329576
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  • Funder: European Commission Project Code: 239993
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  • Funder: French National Research Agency (ANR) Project Code: ANR-22-CE33-0007
    Funder Contribution: 428,270 EUR

    Robotic systems are expected to take a large place in tomorrow’s society, far beyond current industrial robots in tightly controlled factory environments, with large impacts in terms of safety, health at work, comfort and productivity. The motion of robots is typically designed and controlled by specifying numerical objectives and constraints on what they must do, and within which limits. These specifications often conflict, and the actual control must be computed to satisfy all of them in the best possible way. This is naturally achieved by solving a numerical optimization problem. Such problems are often small enough in robotics that they can be solved exactly in theory, but they are always based on models, and by definition, models reflect reality imperfectly, even more so as we get away from tightly controlled (factory) environments. We propose a complete change of paradigm, to acknowledge that we actually solve inaccurate optimization problems that provide inaccurate solutions by construction, and explore the following two hypotheses: (H1) We can obtain the exact same performance with imprecise numerical solutions, (H2) we can obtain these imprecise numerical solutions using less costly numerical methods, which can be computed faster, using less demanding hardware. To the best of our knowledge, these questions have barely been explored and INEXACT will provide the first comprehensive exploration of this topic. Our short-term ambition is to significantly lower the computational requirements for solving control problems, taking advantage of the imprecisions inherent to robotics control to compute appropriate solutions faster. But ultimately, our long-term ambition is to design less fragile, less expensive and less polluting robots, since being less dependent on precise models can make us less dependent on precise and therefore complex, fragile, expensive and resource-demanding mechatronics.

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  • Funder: French National Research Agency (ANR) Project Code: ANR-08-BLAN-0178

    The goal of the project is the design of controllers for swarm robots. Whereas statistical learning from robot logs has encountered several successes to design low-level controllers for a single robot to perform a given task, it is limited first by the quality of the available traces and their adequacy with the target task, and also by possible frequent ambiguities in bothe the environment and the behavioral landscape of the robot. In this context, the originality of this project is twofold: on the one hand, symbolic learning from robot log (either human-driven or controlled by already-learned low-level controllers) will allow the designer to identify landmarks. Such landmarks will then be used to improve the low-level controllers. Secondly, this virtuous circle will be applied not only to single robots, but to swarms of robots, and additional difficulties will rise from the mandatory coordination of the robots, i.e. the necessity to synchronize the different traces before being able to efficiently learn from them collectively. The French team in the project is specialized in statistical learning, with applications in robot control. The Japanese team has a large experience of mining huge amounts of data, and will take care of the symbolic learning part of the project.

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  • Funder: French National Research Agency (ANR) Project Code: ANR-17-CE25-0008
    Funder Contribution: 272,160 EUR

    Fault-tolerant distributed data structures are at the core distributed systems. Due to the multiple sources of non-determinism, their development is challenging. The project aims to increase the confidence we have in distributed implementations of data structures. We think that the difficulty does not only come from the algorithms but from the way we think about distributed systems. We will investigate partially synchronous programming abstractions that reduce the number of interleavings, simplifying the reasoning about distributed systems and their proof arguments. We will use partial synchrony to define reduction theorems from asynchronous semantics to partially synchronous ones, enabling the transfer of proofs from the synchronous world to the asynchronous one. Moreover, we will define a domain specific language, that allows the programmer to focus on the algorithm task, it compiles into efficient asynchronous code, and it is equipped with automated verification engines.

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