递归是有限地描述潜在无限物体的基本范例。由于最先进的强化学习（RL）算法无法直接推理递归，因此他们必须依靠从业者的创造力来设计适当的“平坦”环境代表。由此产生的手动特征结构和近似值繁琐且容易出错。他们缺乏透明度会阻碍可伸缩性。为了克服这些挑战，我们开发了能够在被描述为Markov决策过程集合（MDP）的环境中计算最佳策略的RL算法，这些算法可以递归调用。每个成分MDP的特征是几个进入点和出口点，与这些调用的输入和输出值相对应。这些递归的MDP（或RMDPS）与概率下降系统（呼叫堆栈扮演起作用堆栈的角色）相同，并且可以用递归程序性调用对概率程序进行建模。我们介绍了递归Q学习 - RMDPS的无模型RL算法 - 并证明它在轻度假设下会收敛于有限的，单位和确定性的多EXIT RMDP。

虽然已知辍学是一种成功的正规化技术，但仍缺乏对导致成功的机制的见解。我们介绍了\ emph {重量膨胀}的概念，这增加了由权重协方差矩阵的列或行载体跨越的并行曲线的签名体积，并表明重量膨胀是增加PAC中概括的有效手段。 - bayesian设置。我们提供了一个理论上的论点，即辍学会导致体重扩大和对辍学和体重扩张之间相关性的广泛经验支持。为了支持我们的假设，即可以将重量扩张视为增强的概括能力的\ emph {指示器}，而不仅仅是副产品，我们还研究了实现重量扩展的其他方法（resp。\ contraction \ contraction ），发现它们通常会导致（分别\ \降低）的概括能力。这表明辍学是一种有吸引力的正规化器，因为它是一种用于获得体重扩展的计算廉价方法。这种洞察力证明了辍学者作为正规化器的作用，同时为确定正规化器铺平了道路，这些正规化器有望通过体重扩张来改善概括。

嵌入在自主系统中的机器学习（ML）组件的增加使用 - 所谓的启用学习的系统（LES） - 导致压力需要确保其功能安全性。至于传统的功能安全，在工业和学术界的新兴共识是为此目的使用保证案例。通常，保证案例支持可靠性的支持权，支持安全性，并且可以被视为组织争论和从安全分析和可靠性建模活动产生的证据的结构化方式。虽然这些保证活动传统上由基于协商一致的标准，但由于ML模型的特点和设计，在安全关键应用中，LES构成了新的挑战。在本文中，我们首先向LES提出了一种强调定量方面的总体保证框架，例如，打破系统级安全目标与可靠性指标中所述的组件级要求和支持索赔。然后，我们向ML分类器介绍一种新的模型 - 不可能可靠性评估模型（RAM），该分类器利用操作简档和鲁棒性验证证据。我们讨论了模型假设以及评估我们RAM揭示的ML可靠性的固有挑战，并提出了实用的解决方案。还基于RAM开发了较低ML组件级的概率安全争论。最后，为了评估和展示我们的方法，我们不仅对合成/基准数据集进行实验，还展示了我们对模拟中自动水下车辆的综合案例研究的方法。

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a direct mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. The system was successfully evaluated during the ANA Avatar XPRIZE competition semifinals. In addition, we performed in lab experiments and carried out a small user study with mostly untrained operators.

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn pairwise interactions between each piece of information in each data type, thereby escalating model complexity beyond manageable scales. This has so far precluded a widespread use of multimodal deep learning. Here, we present a new technical approach of "learnable synergies", in which the model only selects relevant interactions between data modalities and keeps an "internal memory" of relevant data. Our approach is easily scalable and naturally adapts to multimodal data inputs from clinical routine. We demonstrate this approach on three large multimodal datasets from radiology and ophthalmology and show that it outperforms state-of-the-art models in clinically relevant diagnosis tasks. Our new approach is transferable and will allow the application of multimodal deep learning to a broad set of clinically relevant problems.

The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary large datasets, but diversity and fidelity are limited, which has recently been addressed by denoising diffusion probabilistic models (DDPMs) whose superiority has been demonstrated on natural images. In this study, we propose Medfusion, a conditional latent DDPM for medical images. We compare our DDPM-based model against GAN-based models, which constitute the current state-of-the-art in the medical domain. Medfusion was trained and compared with (i) StyleGan-3 on n=101,442 images from the AIROGS challenge dataset to generate fundoscopies with and without glaucoma, (ii) ProGAN on n=191,027 from the CheXpert dataset to generate radiographs with and without cardiomegaly and (iii) wGAN on n=19,557 images from the CRCMS dataset to generate histopathological images with and without microsatellite stability. In the AIROGS, CRMCS, and CheXpert datasets, Medfusion achieved lower (=better) FID than the GANs (11.63 versus 20.43, 30.03 versus 49.26, and 17.28 versus 84.31). Also, fidelity (precision) and diversity (recall) were higher (=better) for Medfusion in all three datasets. Our study shows that DDPM are a superior alternative to GANs for image synthesis in the medical domain.

Human-technology collaboration relies on verbal and non-verbal communication. Machines must be able to detect and understand the movements of humans to facilitate non-verbal communication. In this article, we introduce ongoing research on human activity recognition in intralogistics, and show how it can be applied in industrial settings. We show how semantic attributes can be used to describe human activities flexibly and how context informantion increases the performance of classifiers to recognise them automatically. Beyond that, we present a concept based on a cyber-physical twin that can reduce the effort and time necessary to create a training dataset for human activity recognition. In the future, it will be possible to train a classifier solely with realistic simulation data, while maintaining or even increasing the classification performance.