Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.

设置机器人环境快速测试新开发的算法仍然是一个困难且耗时的过程。这给有兴趣执行现实世界机器人实验的研究人员带来了重大障碍。Robotio是一个旨在解决此问题的Python库。它着重于为机器人，抓地力和摄像机等提供常见，简单和结构化的Python接口。这些接口以及这些接口的实现为常见硬件提供了。此启用使用机器人的代码可以在不同的机器人设置上可移植。在建筑方面，Robotio旨在与OpenAI健身房环境以及ROS兼容。提供了这两种示例。该库与许多有用的工具一起融合在一起，例如相机校准脚本和情节记录功能，这些功能进一步支持算法开发。

机器人技术中的一个长期目标是建立可以从使用其板载传感器获得的感知中执行各种日常任务的机器人，并且仅通过自然语言指定。尽管最近通过利用从像素的端到端学习来实现了在语言驱动的机器人技术中的实质性进步，但由于设置的基本差异，没有明确且妥善理解的过程来做出各种设计选择。在本文中，我们对从离线自由模仿数据集中学习语言条件政策的最关键挑战进行了广泛的研究。我们进一步确定了改善性能的架构和算法技术，例如机器人控制学习的层次分解，多模式变压器编码器，离散的潜在计划以及与视频和语言表示一致的自我监视的对比损失。通过将调查的结果与改进的模型组件相结合，我们能够提出一种新颖的方法，该方法在具有挑战性的语言条件长的长摩托器机器人操纵Calvin基准上大大优于最新技术。我们已经开源的实施方式，以促进未来的研究，以学习自然语言连续指定的许多复杂的操纵技能。 http://hulc.cs.uni-freiburg.de可用代码库和训练有素的模型

这项工作提出了一种体现的代理，可以以完全自主的方式将其语义分割网络调整到新的室内环境中。由于语义分割网络无法很好地推广到看不见的环境，因此代理会收集新环境的图像，然后将其用于自我监督的域适应性。我们将其作为一个有益的路径计划问题提出，并提出一种新的信息增益，该信息利用从语义模型中提取的不确定性来安全地收集相关数据。随着域的适应性的进展，这些不确定性会随着时间的推移而发生变化，并且我们系统的快速学习反馈驱使代理收集不同的数据。实验表明，与勘探目标相比，我们的方法更快地适应了新环境，最终性能更高，并且可以成功部署到物理机器人上的现实环境中。

与人类在环境中共存的通用机器人必须学会将人类语言与其在一系列日常任务中有用的看法和行动联系起来。此外，他们需要获取各种曲目的一般专用技能，允许通过遵循无约束语言指示来组成长地平任务。在本文中，我们呈现了凯文（从语言和愿景撰写的行动），是一个露天模拟基准，用于学习Long-Horizo​​ n语言条件的任务。我们的目的是使可以开发能够通过船上传感器解决许多机器人操纵任务的代理商，并且仅通过人类语言指定。 Calvin任务在序列长度，动作空间和语言方面更复杂，而不是现有的视觉和语言任务数据集，并支持灵活的传感器套件规范。我们评估零拍摄的代理商以新颖的语言指示以及新的环境和对象。我们表明，基于多语境模仿学习的基线模型在凯文中表现不佳，表明有很大的空间，用于开发创新代理，了解学习将人类语言与这款基准相关的世界模型。

Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de facto standard for training neural networks without leaking sensitive information about the training data. However, applying it to models for graph-structured data poses a novel challenge: unlike with i.i.d. data, sensitive information about a node in a graph cannot only leak through its gradients, but also through the gradients of all nodes within a larger neighborhood. In practice, this limits privacy-preserving deep learning on graphs to very shallow graph neural networks. We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph. We develop three random-walk-based methods for generating such disjoint subgraphs and perform a careful analysis of the data-generating distributions to provide strong privacy guarantees. Through extensive experiments, we show that our method greatly outperforms the state-of-the-art baseline on three large graphs, and matches or outperforms it on four smaller ones.

A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean space. This kernel is built using the reservoir functional associated with a state-space representation of the Volterra series expansion available for any analytic fading memory filter. It is hence called the Volterra reservoir kernel. Even though the state-space representation and the corresponding reservoir feature map are defined on an infinite-dimensional tensor algebra space, the kernel map is characterized by explicit recursions that are readily computable for specific data sets when employed in estimation problems using the representer theorem. We showcase the performance of the Volterra reservoir kernel in a popular data science application in relation to bitcoin price prediction.

Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and are a key technology in achieving our goals of the German energy transformation and to become less dependent on fossil fuels. Today, the majority of heat pumps in the field are controlled by a simple heating curve, which is a naive mapping of the current outdoor temperature to a control action. A more advanced control approach is model predictive control (MPC) which was applied in multiple research works to heat pump control. However, MPC is heavily dependent on the building model, which has several disadvantages. Motivated by this and by recent breakthroughs in the field, this work applies deep reinforcement learning (DRL) to heat pump control in a simulated environment. Through a comparison to MPC, it could be shown that it is possible to apply DRL in a model-free manner to achieve MPC-like performance. This work extends other works which have already applied DRL to building heating operation by performing an in-depth analysis of the learned control strategies and by giving a detailed comparison of the two state-of-the-art control methods.

Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related approaches for few-shot motion prediction do not incorporate the underlying graph, while it is a common component in classical motion prediction. Furthermore, state-of-the-art methods for few-shot motion prediction are restricted to motion tasks with a fixed output space meaning these tasks are all limited to the same sensor graph. In this work, we propose to extend recent works on few-shot time-series forecasting with heterogeneous attributes with graph neural networks to introduce the first few-shot motion approach that explicitly incorporates the spatial graph while also generalizing across motion tasks with heterogeneous sensors. In our experiments on motion tasks with heterogeneous sensors, we demonstrate significant performance improvements with lifts from 10.4% up to 39.3% compared to best state-of-the-art models. Moreover, we show that our model can perform on par with the best approach so far when evaluating on tasks with a fixed output space while maintaining two magnitudes fewer parameters.

This project leverages advances in multi-agent reinforcement learning (MARL) to improve the efficiency and flexibility of order-picking systems for commercial warehouses. We envision a warehouse of the future in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput) under given resource constraints. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, the MARL framework can be flexibly applied to any warehouse configuration (e.g. size, layout, number/types of workers, item replenishment frequency) and the agents learn via a process of trial-and-error how to optimally cooperate with one another. This paper details the current status of the R&D effort initiated by Dematic and the University of Edinburgh towards a general-purpose and scalable MARL solution for the order-picking problem in realistic warehouses.