Edge computing is becoming more and more popular among researchers who seek to take advantage of the edge resources and the minimal time delays, in order to run their robotic applications more efficiently. Recently, many edge architectures have been proposed, each of them having their advantages and disadvantages, depending on each application. In this work, we present two different edge architectures for controlling the trajectory of an Unmanned Aerial Vehicle (UAV). The first architecture is based on docker containers and the second one is based on kubernetes, while the main framework for operating the robot is the Robotic Operating System (ROS). The efficiency of the overall proposed scheme is being evaluated through extended simulations for comparing the two architectures and the overall results obtained.

边缘计算是一项有前途的技术，可以在需要瞬时数据处理的技术领域提供新功能。机器和深度学习等领域的研究人员对其应用程序进行了广泛的边缘和云计算，这主要是由于他们提供的大量计算和存储资源。目前，机器人技术也正在寻求利用这些功能，并且随着5G网络的开发，可以克服该领域的一些现有限制。在这种情况下，重要的是要知道如何利用新兴的边缘体系结构，当今存在哪些类型的边缘体系结构和平台，以及哪些可以并且应该基于每个机器人应用程序使用。一般而言，边缘平台可以以不同的方式实现和使用，尤其是因为有几个提供商提供或多或少提供的一组服务以及一些基本差异。因此，本研究针对那些从事下一代机器人系统开发的人解决了这些讨论，并将有助于理解每个边缘计算体系结构的优势和缺点，以便明智地选择适合每个应用程序的功能。

在本文中，我们提出了一种控制策略，以解决模仿卫星运动的平面浮动平台（Slider）的安全自动对接问题。 Slider采用拟议的策略，以正确的方向接近对接端口，保持安全的距离，同时始终在整个对接操作中的对接端口上保持视觉锁定。控制障碍功能旨在强加安全，进近方向和视觉锁定限制。滑块的三个控制输入在执行约束时共享三个障碍函数。事实证明，控制输入以无冲突的方式共享，以渲染定义安全性和视觉锁定约束向前不变的集合，并在建立有限的时间收敛到视觉锁定模式时。无冲突的输入共享确保了二次程序的可行性，该程序为标称控制器生成最小侵入性的校正，旨在跟踪对接端口，因此在整个码头操作中都尊重屏障约束。提出的控制设计方法的功效通过各种模拟得到验证。

Model compression via quantization and sparsity enhancement has gained an immense interest to enable the deployment of deep neural networks (DNNs) in resource-constrained edge environments. Although these techniques have shown promising results in reducing the energy, latency and memory requirements of the DNNs, their performance in non-ideal real-world settings (such as in the presence of hardware faults) is yet to be completely understood. In this paper, we investigate the impact of bit-flip and stuck-at faults on activation-sparse quantized DNNs (QDNNs). We show that a high level of activation sparsity comes at the cost of larger vulnerability to faults. For instance, activation-sparse QDNNs exhibit up to 17.32% lower accuracy than the standard QDNNs. We also establish that one of the major cause of the degraded accuracy is sharper minima in the loss landscape for activation-sparse QDNNs, which makes them more sensitive to perturbations in the weight values due to faults. Based on this observation, we propose the mitigation of the impact of faults by employing a sharpness-aware quantization (SAQ) training scheme. The activation-sparse and standard QDNNs trained with SAQ have up to 36.71% and 24.76% higher inference accuracy, respectively compared to their conventionally trained equivalents. Moreover, we show that SAQ-trained activation-sparse QDNNs show better accuracy in faulty settings than standard QDNNs trained conventionally. Thus the proposed technique can be instrumental in achieving sparsity-related energy/latency benefits without compromising on fault tolerance.

We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Existing optimization-based approaches simulating contact between convex primitives rely on a bilevel formulation that separates collision detection and contact simulation. These approaches are unreliable in realistic contact simulation scenarios because isolating the collision detection problem introduces contact location non-uniqueness. Our approach combines contact simulation and collision detection into a unified single-level optimization problem. This disambiguates the collision detection problem in a physics-informed manner. Compared to previous differentiable simulation approaches, our formulation features improved simulation robustness and a reduction in computational complexity by more than an order of magnitude. We illustrate the contact and collision differentiability on a robotic manipulation task requiring optimization-through-contact. We provide a numerically efficient implementation of our formulation in the Julia language called Silico.jl.

Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computationally expensive experiments. In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers. We suggest making caching an integral part of the research software development process, even before the first line of code is written. This article outlines caching recommendations for developing research software in data science projects. Our recommendations provide a perspective to circumvent common problems such as propriety dependence, speed, etc. At the same time, caching contributes to the reproducibility of experiments in the open science workflow. Concerning the four guiding principles, i.e., Findability, Accessibility, Interoperability, and Reusability (FAIR), we foresee that including the proposed recommendation in a research software development will make the data related to that software FAIRer for both machines and humans. We exhibit the usefulness of some of the proposed recommendations on our recently completed research software project in mathematical information retrieval.

尽管进行了数十年的研究，但现有的导航系统在野外部署时仍然面临现实世界中的挑战，例如在混乱的家庭环境或人类占领的公共场所中。为了解决这个问题，我们提出了一类新的隐式控制政策，将模仿学习的好处与模型预测控制（MPC）的系统约束的强大处理结合在一起。我们的方法称为Performer-MPC，使用了通过表演者提供的视觉上下文嵌入的学习成本函数（一种低级隐式意见变压器）。我们共同训练成本函数并构建依靠它的控制器，有效地端到端解决相应的双层优化问题。我们表明，由此产生的策略通过利用一些在不同挑战的现实世界情景中利用一些专家演示来提高标准MPC绩效。与标准的MPC政策相比，表演者MPC在混乱的环境中实现了40％的目标，而在人类浏览时，社交指标的目标> 65％。

灰度图像着色是AI在信息恢复中的引人入胜的应用。该问题的天生性质不良的性质使其更具挑战性，因为输出可能是多模式的。目前正在使用的基于学习的方法为直接情况产生可接受的结果，但在没有明确的图形分离的情况下通常无法恢复上下文信息。同样，由于在完整图像特征上训练的单个模型不足以学习各种数据模式，因此图像遭受了颜色出血和饱和背景。为了解决这些问题，我们提出了一个基于GAN的配色框架。在我们的方法中，每个量身定制的GAN管道都会使前景（使用对象级特征）或背景（使用全图像功能）着色。前景管道采用了一个具有自我注意事项的残留无UNET作为其发电机，使用了全图像功能和可可数据集中的相应对象级特征训练。背景管道依赖于该位置数据集的全图像功能和其他培训示例。我们设计了一个基于密集的融合网络，以通过基于特征的融合来获得最终的有色图像。我们显示了通常用于评估多模式问题（例如图像着色）并使用多个感知指标对我们的框架进行广泛的绩效评估的非感知评估指标的缺点。我们的方法的表现优于大多数基于学习的方法，并且产生的结果与最新的方法相当。此外，我们进行了运行时分析，并获得了每个图像的平均推理时间24ms。

极端分类（XC）试图用最大的标签集中标记标签的子集标记数据点。通过使用稀疏，手工制作的功能的XC方法优越，用密集，学习的数据来进行深度XC，以数据点和标签的形式吸引了很多关注。负挖掘技术已成为所有深XC方法的关键组成部分，使它们可以扩展到数百万个标签。然而，尽管最近进步，但培训具有大型编码器体系结构（例如变形金刚）的深入XC模型仍然具有挑战性。本文确定，流行负面挖掘技术的内存通常迫使小型批量尺寸保持小且缓慢的训练。作为回应，本文介绍了Ngame，这是一种轻巧的迷你批次创建技术，可证明可证明准确的内部负面样品。这使得与现有负面采样技术相比，具有更大的迷你批次培训，提供更快的收敛性和更高的精度。发现Ngame的准确性比各种基准数据集的最先进方法要高16％，以进行极端分类，并且在回答搜索引擎查询以响应用户网页时检索搜索引擎查询更准确3％显示个性化广告。在流行搜索引擎的实时A/B测试中，Ngame在点击率率中的收益最高可达23％。