Learning robust and efficient point cloud representations

L'abstract è presente nell'allegato / the abstract is in the attachmen

Opportunities for graph learning in robotics

In the last few years, robotics highly benefited from the use of machine and deep learning to process data stream captured by robots during their tasks. Yet, encoding data in grids (images) or vectors (time-series) significantly limits the type of data that can be processed to euclidean only. To unlock the potential of deep learning also to unstructured data, such as point clouds or functional relations, a rising - yet under-explored - approach lies on the use of graph neural networks (GNNs). With this manuscript, we intend to deliver a brief introduction to GNNs for robotics applications, together with a concise revision of notable applications in the field, with the aim of fostering the use of this learning strategy in a wider context and highlighting potential future research directions

Graph learning in robotics: a survey

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applications. To fully unlock their potential, hence, we propose a review of graph neural architectures from a robotics perspective. The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings, related for example to the integration of perception, decision-making, and control. Finally, the paper provides an extensive review of various robotic applications that benefit from learning on graph structures, such as bodies and contacts modelling, robotic manipulation, action recognition, fleet motion planning, and many more. This survey aims to provide readers with a thorough understanding of the capabilities and limitations of graph neural architectures in robotics, and to highlight potential avenues for future research

Point Cloud Normal Estimation with Graph-Convolutional Neural Networks

Surface normal estimation is a basic task for many point cloud processing algorithms. However, it can be challenging to capture the local geometry of the data, especially in presence of noise. Recently, deep learning approaches have shown promising results. Nevertheless, applying convolutional neural networks to point clouds is not straightforward, due to the irregular positioning of the points. In this paper, we propose a normal estimation method based on graph-convolutional neural networks to deal with such irregular point cloud domain. The graph-convolutional layers build hierarchies of localized features to solve the estimation problem. We show state-ofthe-art performance and robust results even in presence of noise

Entropic Score metric: Decoupling Topology and Size in Training-free NAS

Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and computational resources to generate accurate models. To overcome these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations; ii) a cyclic search algorithm to separately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet classification.Comment: 10 pages, 3 figure

GraphPointNet: Graph Convolutional Neural Network for Point Cloud Denoising

The proposed project is focused on developing a novel neural network for point cloud denoising based on graph convolution operations

Graph learning in robotics: a survey

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applications. To fully unlock their potential, hence, we propose a review of graph neural architectures from a robotics perspective. The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings, related for example to the integration of perception, decision-making, and control. Finally, the paper provides an extensive review of various robotic applications that benefit from learning on graph structures, such as bodies and contacts modelling, robotic manipulation, action recognition, fleet motion planning, and many more. This survey aims to provide readers with a thorough understanding of the capabilities and limitations of graph neural architectures in robotics, and to highlight potential avenues for future research

Universitates e baronie. Arte e architettura in Abruzzo e nel Regno al tempo dei Durazzo

Convegno dedicato ai fatti artistici durazzeschi in Abruzzo, ma anche in Italia meridionale, al fine di riscattare una stagione culturale spesso dimenticata dalla storiografia, che comunque costituisce agli inizi del Quattrocento l'anello di congiunzione tra l'età tardogotica e i prodromi del Rinascimento nel Regno di Sicilia

Entropic Score Metric: Decoupling Topology and Size in Training-Free NAS

Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typi- cal of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and com- putational resources to generate accurate models. To over- come these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise en- tropy of its activations; ii) a cyclic search algorithm to sep- arately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capa- bility to completely design high-performance Hybrid Trans- formers for edge applications in less than 1 GPU hour, re- sulting in the fastest and most accurate NAS method for Im- ageNet classification

Impact of Nitric Oxide Bioavailability on the Progressive Cerebral and Peripheral Circulatory Impairments During Aging and Alzheimer's Disease

Advanced aging, vascular dysfunction, and nitric oxide (NO) bioavailability are recognized risk factors for Alzheimer's disease (AD). However, the contribution of AD, per se, to this putative pathophysiological mechanism is still unclear. To better answer this point, we quantified cortical perfusion with arterial spin labeling (PVC-CBF), measured ultrasound internal carotid (ICA), and femoral (FA) artery blood flow in a group of patients with similar age (~78 years) but different cognitive impairment (i.e., mild cognitive impairment MCI, mild AD-AD1, moderate AD-AD2, and severe AD-AD3) and compared them to young and healthy old (aged-matched) controls. NO-metabolites and passive leg-movement (PLM) induced hyperemia were used to assess systemic vascular function. Ninety-eight individuals were recruited for this study. PVC-CBF, ICA, and FA blood flow were markedly (range of 9–17%) and significantly (all p < 0.05) reduced across the spectrum from YG to OLD, MCI, AD1, AD2, AD3 subjects. Similarly, plasma level of nitrates and the values of PLM were significantly reduced (range of 8–26%; p < 0.05) among the six groups. Significant correlations were retrieved between plasma nitrates, PLM and PVC-CBF, CA, and FA blood flow. This integrative and comprehensive approach to vascular changes in aging and AD showed progressive changes in NO bioavailability and cortical, extracranial, and peripheral circulation in patients with AD and suggested that they are directly associated with AD and not to aging. Moreover, these results suggest that AD-related impairments of circulation are progressive and not confined to the brain. The link between cardiovascular and the central nervous systems degenerative processes in patients at different severity of AD is likely related to the depletion of NO