Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation. We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement. We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages. Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.Comment: Accepted at ICRA 2018; 8 pages, 4 figures, 1 algorithm; Video at https://youtu.be/xo8mUIZTvNE ; Spotlight ICRA presentation https://youtu.be/iiVaV-U6Kq

Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction

We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. The direction can come from an Inertial Measurement Unit that is a standard component of recent consumer devices, e.g., smartphones. We provide an exhaustive analysis of minimal line configurations and derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. Additionally, we design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization. Combining all solvers in a hybrid robust estimator, our method achieves increased accuracy even with a rough prior. Experiments on synthetic and real-world datasets demonstrate the superior accuracy of our method compared to the state of the art, while having comparable runtimes. We further demonstrate the applicability of our solvers for relative rotation estimation. The code is available at https://github.com/cvg/VP-Estimation-with-Prior-Gravity.Comment: Accepted at ICCV 202

DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD.Comment: Accepted at CVPR 202

Quality Diversity for Multi-task Optimization

International audienceQuality Diversity (QD) algorithms are a recent family of optimization algorithms that search for a large set of diverse but high-performing solutions. In some specific situations, they can solve multiple tasks at once. For instance, they can find the joint positions required for a robotic arm to reach a set of points, which can also be solved by running a classic optimizer for each target point. However, they cannot solve multiple tasks when the fitness needs to be evaluated independently for each task (e.g., optimizing policies to grasp many different objects). In this paper, we propose an extension of the MAP-Elites algorithm, called Multi-task MAP-Elites, that solves multiple tasks when the fitness function depends on the task. We evaluate it on a simulated parameterized planar arm (10-dimensional search space; 5000 tasks) and on a simulated 6-legged robot with legs of different lengths (36-dimensional search space; 2000 tasks). The results show that in both cases our algorithm outperforms the optimization of each task separately with the CMA-ES algorithm

La Préhistoire en Bourgogne : état des connaissances et bilan 1994-2005

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La Préhistoire en Bourgogne : État des connaissances et bilan 1994-2005

Impulsé par la Direction des patrimoines (Ministère de la Culture et de la Communication), le bilan scientifique régional 1994-2005 s’est traduit, pour la Préhistoire bourguignonne, par un volumineux travail de synthèse collective, associant la majorité des chercheurs concernés. Bien qu’elle ait traîné en longueur, cette rédaction a permis de nombreux échanges et la confrontation de points de vue parfois différents, mais finalement complémentaires, orientés vers des objectifs communs. L’ouvrage est découpé en deux grandes parties, Paléolithique / Mésolithique d’une part, et Néolithique d’autre part ; chacune issue d’un processus de maturation et de rédaction différent. Dans la première, l’accent est mis sur une description synthétique exhaustive des recherches menées dans la période considérée, selon un découpage à la fois thématique et chronologique. Dans la seconde, le choix s’est porté sur un état des lieux similaire, mais organisé géographiquement par grands bassins versants (Seine-Yonne, Loire et Saône). Cet état des lieux est replacé dans l’histoire régionale des recherches néolithiques et complété par des corpus synthétiques, totalement inédits à ce jour (datations radiocarbone, bibliographie exhaustive sur le Néolithique bourguignon). Loin de s’arrêter à un simple état des lieux, lui-même complètement inédit, les auteurs se sont livrés à une réflexion prospective et proposent les thèmes prioritaires ou les orientations de ce que devrait être la recherche programmée et préventive des années à venir. Par la masse considérable d’informations actualisées qu’il propose, ce volume démontre la richesse et la vitalité de la recherche archéologique bourguignonne, qu’il s’agisse des grottes d’Arcy-sur-Cure, du Paléolithique supérieur de Saône-et-Loire, des multiples occupations néolithiques, domestiques ou funéraires, des vallées de l’Yonne ou de la Saône

14. Le Néolithique du bassin versant de la Loire

Les opérations réalisées et les informations acquises pour cette zone géographique sont peu nombreuses, aussi bien pour la décennie concernée par le bilan que pour l’état des connaissances sur ce bassin versant en général. Plusieurs facteurs peuvent expliquer cette pauvreté documentaire et les préhistoriens intervenant dans ces régions sont très peu nombreux. Il s’agit souvent de prospecteurs dont les liens avec la communauté scientifique sont très ténus. Dans la Nièvre, par exemple, la seule..

Altered parabrachial nucleus nociceptive processing may underlie central pain in Parkinson’s disease

Abstract The presence of central neuropathic pain in Parkinson’s disease suggests that the brain circuits that allow us to process pain could be dysfunctional in the disorder. However, there is to date no clear pathophysiological mechanism to explain these symptoms. In this work, we present evidence that the dysfunction of the subthalamic nucleus and/or substantia nigra pars reticulata may impact nociceptive processing in the parabrachial nucleus (PBN), a low level primary nociceptive structure in the brainstem, and induce a cellular and molecular neuro-adaptation in this structure. In rat models of Parkinson’s disease with a partial dopaminergic lesion in the substantia nigra compacta, we found that the substantia nigra reticulata showed enhanced nociceptive responses. Such responses were less impacted in the subthalamic nucleus. A total dopaminergic lesion produced an increase in the nociceptive responses as well as an increase of the firing rate in both structures. In the PBN, inhibited nociceptive responses and increased expression of GABAA receptors were found following a total dopaminergic lesion. However, neuro-adaptations at the level of dendritic spine density and post-synaptic density were found in both dopaminergic lesion groups. These results suggest that the molecular changes within the PBN following a larger dopaminergic lesion, such as increased GABAA expression, is a key mechanism to produce nociceptive processing impairment, whilst other changes may protect function after smaller dopaminergic lesions. We also propose that these neuro-adaptations follow increased inhibitory tone from the substantia nigra pars reticulata and may represent the mechanism generating central neuropathic pain in Parkinson’s disease

Avant-propos

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