学习多样化的技能是机器人技术的主要挑战之一。为此，模仿学习方法取得了令人印象深刻的结果。这些方法需要明确标记的数据集或采用一致的技能执行，以使学习和积极控制单个行为，从而限制其适用性。在这项工作中，我们提出了一种合作的对抗方法，用于从未标记的数据集中获得可控技能的单一多功能策略，该数据集包含各种状态过渡模式，通过最大化其可区分性。此外，我们表明，通过在生成的对抗性模仿学习框架中利用无监督的技能发现，新颖而有用的技能随着成功的任务实现而出现。最后，在示威中编码的各种技能的忠实复制中，对获得的多功能策略进行了测试，并呈现了忠实的复制。

学习敏捷技能是机器人技术的主要挑战之一。为此，加强学习方法取得了令人印象深刻的结果。这些方法需要根据奖励功能或可以在模拟中查询的专家来提供明确的任务信息，以提供目标控制输出，从而限制其适用性。在这项工作中，我们提出了一种生成的对抗方法，用于从部分和潜在的物理不兼容的演示中推断出奖励功能，以成功地获得参考或专家演示的成功技能。此外，我们表明，通过使用Wasserstein gan公式和从以粗糙和部分信息为输入的示范中进行过渡，我们能够提取强大的策略并能够模仿证明的行为。最后，在一个名为Solo 8的敏捷四倍的机器人上测试了所获得的技能，例如后空飞弹，并对手持人类示范的忠实复制进行了测试。

尽管腿部机器人运动取得了进展，但在未知环境中的自主导航仍然是一个空旷的问题。理想情况下，导航系统在不确定性下在安全限制内运行时，利用机器人的运动功能的全部潜力。机器人必须感知和分析周围地形的遍历性，这取决于硬件，运动控制和地形特性。它可能包含有关穿越地形所需的风险，能量或时间消耗的信息。为了避免手工制作的遍历成本功能，我们建议通过使用物理模拟器在随机生成的地形上模拟遍历的遍历策略，以收集有关机器人和运动策略的遍历性信息。在现实中使用的相同的运动策略并行控制了数千个机器人，以获得57年的现实运动体验。对于在Real机器人上的部署，培训了一个稀疏的卷积网络，以预测模拟的遍历性成本，该成本是根据已部署的运动策略量身定制的，它是从环境的完全几何表示，以3D素体占用图的形式。该表示避免了对常用的高程图的需求，在存在悬垂障碍物以及多层或低天花板方案的情况下，这些图形图很容易出错。在各种室内和自然环境中，为腿部机器人Anymal的路径计划证明了拟议的遍历性预测网络的有效性。

语义分段网络通常在部署期间预先培训并且未更新。因此，如果训练数据的分布偏离机器人操作期间遇到的那个，则通常发生错误分类。我们建议通过将神经网络调整到机器人在部署期间的环境中来缓解此问题，而无需对外监督。利用互补数据表示，通过概率地累积在体积3D地图中的连续2D语义预测来生成监督信号。然后，我们在累积的语义地图的渲染上重新培训网络，有效地解决歧义并通过3D表示来执行多视图一致性。为了在进行网络适应时保留先前学习的知识，我们采用了基于体验重放的持续学习策略。通过广泛的实验评估，我们对Scannet DataSet和RGB-D传感器记录的内部数据显示了对现实世界室内场景的成功适应。与固定的预训练的神经网络相比，我们的方法平均增加了分割性能11.8％，同时有效地保留了从预训练前数据集的知识。

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.

In the era of noisy intermediate scale quantum devices, variational quantum circuits (VQCs) are currently one of the main strategies for building quantum machine learning models. These models are made up of a quantum part and a classical part. The quantum part is given by a parametrization $U$, which, in general, is obtained from the product of different quantum gates. By its turn, the classical part corresponds to an optimizer that updates the parameters of $U$ in order to minimize a cost function $C$. However, despite the many applications of VQCs, there are still questions to be answered, such as for example: What is the best sequence of gates to be used? How to optimize their parameters? Which cost function to use? How the architecture of the quantum chips influences the final results? In this article, we focus on answering the last question. We will show that, in general, the cost function will tend to a typical average value the closer the parameterization used is from a $2$-design. Therefore, the closer this parameterization is to a $2$-design, the less the result of the quantum neural network model will depend on its parametrization. As a consequence, we can use the own architecture of the quantum chips to defined the VQC parametrization, avoiding the use of additional swap gates and thus diminishing the VQC depth and the associated errors.

Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we get with a single GPU in just one day? We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for a single day on a single consumer GPU. Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario. We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings. Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting.

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.

In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.