Reinforcement Learning (RL) can solve complex tasks but does not intrinsically provide any guarantees on system behavior. For real-world systems that fulfill safety-critical tasks, such guarantees on safety specifications are necessary. To bridge this gap, we propose a verifiably safe RL procedure with probabilistic guarantees. First, our approach probabilistically verifies a candidate controller with respect to a temporal logic specification, while randomizing the controller's inputs within a bounded set. Then, we use RL to improve the performance of this probabilistically verified, i.e. safe, controller and explore in the same bounded set around the controller's input as was randomized over in the verification step. Finally, we calculate probabilistic safety guarantees with respect to temporal logic specifications for the learned agent. Our approach is efficient for continuous action and state spaces and separates safety verification and performance improvement into two independent steps. We evaluate our approach on a safe evasion task where a robot has to evade a dynamic obstacle in a specific manner while trying to reach a goal. The results show that our verifiably safe RL approach leads to efficient learning and performance improvements while maintaining safety specifications.

信号时间逻辑的鲁棒性不仅评估信号是否遵守规范，而且还提供了对公式的满足或违反的量度。鲁棒性的计算基于评估潜在谓词的鲁棒性。但是，通常以无模型方式（即不包括系统动力学）定义谓词的鲁棒性。此外，精确定义复杂谓词的鲁棒性通常是不平凡的。为了解决这些问题，我们提出了模型预测鲁棒性的概念，该概念通过考虑基于模型的预测，它与以前的方法相比提供了一种更系统的评估鲁棒性的方法。特别是，我们使用高斯过程回归来基于预定的预测来学习鲁棒性，以便可以在线上有效地计算鲁棒性值。我们评估了对自动驾驶用例的方法，该案例用在记录的数据集上使用形式的交通规则中使用的谓词来评估我们的方法，这与传统方法相比，在表达性方面相比，我们的方法优势。通过将我们的鲁棒性定义纳入轨迹规划师，自动驾驶汽车比数据集中的人类驾驶员更强大地遵守交通规则。

当今，机器人技术的新型机器人运动学和基于学习的应用程序的开发几乎完全在模拟中进行，然后才在现实世界中实施。特别是，与传统的操纵器相比，模块化可重构机器人（MRR）是工业机器人技术的令人兴奋的创新，有望更大的灵活性，提高可维护性和成本效益。但是，几十年来，没有像为机器人操纵器对模块进行模拟和模型组件的工具或标准化方法。我们介绍了工业模块化机器人技术的工具箱（Timor），这是一种python工具箱，可弥合此间隙并将模块化机器人技术集成在现有的仿真和优化管道中。我们的开源库配备了各种示例和教程，并且可以轻松地与现有的仿真工具集成在一起 - 尤其是通过提供任意模块化机器人组件的URDF导出，从而使快速模型生成。

我们提出了一种新的方法，可以通过具有relu，sigmoid或双曲线切线激活功能的神经网络有效地计算图像的紧密非凸面。特别是，我们通过多项式近似来抽象每个神经元的输入输出关系，该近似是使用多项式界定的基于设定的方式进行评估的。我们提出的方法特别适合于对神经网络控制系统的可及性分析，因为多项式地位型能够捕获两者中的非共鸣性，通过神经网络以及可触及的集合。与各种基准系统上的其他最新方法相比，我们证明了方法的卓越性能。

未来的电力系统将在很大程度上依赖于具有很高分散的可再生能源和能源存储系统的微网格。在这种情况下，高复杂性和不确定性可能使常规的权力调度策略不可行。基于加强学习者（RL）控制器可以应对这一挑战，但是，本身不能提供安全保证，从而阻止其在实践中的部署。为了克服这一限制，我们提出了一个正式验证的RL控制器进行经济调度。我们通过编码岛屿意外事件的时间依赖性约束来扩展常规约束。使用基于集合的向后触及性分析来计算偶性约束，RL代理的动作通过安全层进行验证。不安全的动作被投影到安全的动作空间中，同时利用受限的扎根设置表示表示计算效率。使用现实世界测量值在住宅用例上证明了开发的方法。

当前，选择一个最佳机器人并为给定任务配置为给定任务是由人类专业知识或反复试验完成的。为了评估机器人对特定任务的自动选择和适应，我们引入了一个基准套件，其中包含用于机器人，环境和任务描述的通用格式。我们的基准套件对于模块化机器人特别有用，其中机器人的配置本身会创建许多其他参数以优化。基准定义了此优化问题，并促进了解决方案算法的比较。所有基准都可以通过Cobra.cps.cit.tum.de访问，该网站可方便地共享，参考和比较解决方案。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Vision transformers have emerged as powerful tools for many computer vision tasks. It has been shown that their features and class tokens can be used for salient object segmentation. However, the properties of segmentation transformers remain largely unstudied. In this work we conduct an in-depth study of the spatial attentions of different backbone layers of semantic segmentation transformers and uncover interesting properties. The spatial attentions of a patch intersecting with an object tend to concentrate within the object, whereas the attentions of larger, more uniform image areas rather follow a diffusive behavior. In other words, vision transformers trained to segment a fixed set of object classes generalize to objects well beyond this set. We exploit this by extracting heatmaps that can be used to segment unknown objects within diverse backgrounds, such as obstacles in traffic scenes. Our method is training-free and its computational overhead negligible. We use off-the-shelf transformers trained for street-scene segmentation to process other scene types.

The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.

Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot with respect to its external forces. This compliant robot behavior has the added benefit of allowing physical human guidance of the robot. In this paper, we propose a C++ implementation of compliance control valid for any torque-commanded robotic manipulator. The proposed controller implements Cartesian impedance control to track a desired end-effector pose. Additionally, joint impedance is projected in the nullspace of the Cartesian robot motion to track a desired robot joint configuration without perturbing the Cartesian motion of the robot. The proposed implementation also allows the robot to apply desired forces and torques to its environment. Several safety features such as filtering, rate limiting, and saturation are included in the proposed implementation. The core functionalities are in a re-usable base library and a Robot Operating System (ROS) ros_control integration is provided on top of that. The implementation was tested with the KUKA LBR iiwa robot and the Franka Emika Robot (Panda) both in simulation and with the physical robots.