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Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation. We draw inspiration from neuroscience, and show how this can be achieved in an unsupervised generative model(More)
We compare two well-known algorithms for locating odor sources in environments with a main wind flow. Their plume tracking performance is tested through systematic experiments with real robots in a wind tunnel under laminar flow condition. We present the system setup and show the wind and odor profiles. The results are then compared in terms of time and(More)
We introduce a novel case study in which a group of minia-turized robots screen an environment for undesirable agents, and destroy them. Because miniaturized robots are usually endowed with reactive controllers and minimalist sensing and actuation capabilities, they must collaborate in order to achieve their task efficiently. In this paper, we show how(More)
Project description We propose a new modeling framework inspired by chemical reaction processes. Our approach consists in defining the processes and the interactions within the system in term of reactions. Such a definition can be applied to many systems, ranging from biochemical systems to swarm robotics. In particular, we aim at exploiting the toolbox(More)
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