对于自动导航和机器人应用，正确感知环境至关重要。存在许多用于此目的的感应方式。近年来，一种使用的方式是空中成像声纳。它在具有灰尘或雾之类的粗糙条件的复杂环境中是理想的选择。但是，就像大多数传感方式一样，要感知移动平台周围的完整环境，需要多个此类传感器来捕获完整的360度范围。当前，用于创建此数据的处理算法不足以以相当快的更新速率为多个传感器这样做。此外，需要一个灵活而健壮的框架，以轻松地将多个成像声纳传感器实现到任何设置中，并为数据提供多种应用程序类型。在本文中，我们提出了一个专为这种新型传感方式而设计的传感器网络框架。此外，提出了在图形处理单元上的处理算法的实现，以减少计算时间，以便以足够高的更新速率实时处理一个或多个成像声纳传感器。

估计六级自由人体姿势的系统已有二十年多了。诸如运动捕获摄像机，高级游戏外围设备以及最近的深度学习技术和虚拟现实系统等技术都显示出令人印象深刻的结果。但是，大多数提供高精度和高精度的系统都是昂贵的，并且不容易操作。最近，已经进行了研究以使用HTC Vive虚拟现实系统估算人体姿势。该系统显示出准确的结果，同时将成本保持在1000美元以下。该系统使用光学方法。通过在接收器硬件上使用照片二极管来跟踪两个发射器设备发射红外脉冲和激光平面。以前开发了使用这些发射器设备与低成本定制接收器硬件结合使用的系统，但需要手动测量发射机设备的位置和方向。这些手动测量可能很耗时，容易出错，并且在特定设置中不可能。我们提出了一种算法，以使用自定义接收器/校准硬件的任何选择的环境中自动校准发射机设备的姿势。结果表明，校准在各种设置中起作用，同时比手动测量所允许的更准确。此外，校准运动和速度对结果的精度没有明显的影响。

在自主移动平台的各种和动态室内环境中导航仍然是一项复杂的任务。尤其是当条件恶化时，典型的传感器模式可能无法最佳运行，随后为安全导航控制提供了INAPT输入。在这项研究中，我们提出了一种使用分层控制系统具有单个或几个声纳传感器的移动平台进行动态室内环境导航的方法。这些传感器可以在雨，雾，灰尘或污垢等条件下运行。不同的控制层，例如避免碰撞和行为后的走廊，根据声流图像的融合中的声流队列被激活。这项工作的新颖性使这些传感器可以自由放置在移动平台上，并提供了基于移动平台周围分区系统设计最佳导航结果的框架。本文介绍的是使用的声流模型以及分层控制器的设计。在模拟中的验证旁边，在真实的办公室环境中使用具有一个，两个或三个声纳传感器的真实移动平台在真实的办公室环境中实现了实施和验证，并通过2D导航实时实时。在模拟和实际实验中均已验证了多个传感器布局，以证明控制器和传感器融合的模块化方法最佳起作用。这项工作的结果显示了具有动态对象的室内环境的稳定且安全的导航。

在空间上导航和动态环境是自主代理的关键任务之一。在本文中，我们提出了一种新颖的方法，该方法可以通过一个或多个3D-sonar传感器导航移动平台。移动移动平台，然后在其上移动任何3D-sonar传感器，将随着传感器读取中回声反射的时间而创建签名变化。提出了一种方法，可以为任何运动类型创建这些签名变化的预测模型。此外，该模型是自适应的，可用于移动平台上一个或多个声纳传感器的任何位置和方向。我们建议使用这种自适应模型并将所有感官读数融合来创建一个分层的控制系统，允许移动平台执行一组原始运动，例如避免碰撞，避免障碍物，避开障碍物，跟随墙壁和走廊跟随行为，以动态移动环境导航环境其中的对象。本文描述了整个导航模型的基本理论基础，并在模拟环境中验证了它，结果表明该系统稳定，并为一个或多个声纳传感器的多种测试空间配置提供了预期的行为，可以完成自主导航任务。

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

The xView2 competition and xBD dataset spurred significant advancements in overhead building damage detection, but the competition's pixel level scoring can lead to reduced solution performance in areas with tight clusters of buildings or uninformative context. We seek to advance automatic building damage assessment for disaster relief by proposing an auxiliary challenge to the original xView2 competition. This new challenge involves a new dataset and metrics indicating solution performance when damage is more local and limited than in xBD. Our challenge measures a network's ability to identify individual buildings and their damage level without excessive reliance on the buildings' surroundings. Methods that succeed on this challenge will provide more fine-grained, precise damage information than original xView2 solutions. The best-performing xView2 networks' performances dropped noticeably in our new limited/local damage detection task. The common causes of failure observed are that (1) building objects and their classifications are not separated well, and (2) when they are, the classification is strongly biased by surrounding buildings and other damage context. Thus, we release our augmented version of the dataset with additional object-level scoring metrics https://gitlab.kitware.com/dennis.melamed/xfbd to test independence and separability of building objects, alongside the pixel-level performance metrics of the original competition. We also experiment with new baseline models which improve independence and separability of building damage predictions. Our results indicate that building damage detection is not a fully-solved problem, and we invite others to use and build on our dataset augmentations and metrics.

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.

Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

Database research can help machine learning performance in many ways. One way is to design better data structures. This paper combines the use of incremental computation and sequential and probabilistic filtering to enable "forgetful" tree-based learning algorithms to cope with concept drift data (i.e., data whose function from input to classification changes over time). The forgetful algorithms described in this paper achieve high time performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with at most a 2% loss of accuracy, or at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications.

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.