With more and more data being collected, data-driven modeling methods have been gaining in popularity in recent years. While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness. On the other hand, classical black-box methods, typically relying on Neural Networks (NNs) nowadays, often achieve impressive performance, even at scale, by deriving statistical patterns from data. However, they remain completely oblivious to the underlying physical laws, which may lead to potentially catastrophic failures if decisions for real-world physical systems are based on them. Physically Consistent Neural Networks (PCNNs) were recently developed to address these aforementioned issues, ensuring physical consistency while still leveraging NNs to attain state-of-the-art accuracy. In this work, we scale PCNNs to model building temperature dynamics and propose a thorough comparison with classical gray-box and black-box methods. More precisely, we design three distinct PCNN extensions, thereby exemplifying the modularity and flexibility of the architecture, and formally prove their physical consistency. In the presented case study, PCNNs are shown to achieve state-of-the-art accuracy, even outperforming classical NN-based models despite their constrained structure. Our investigations furthermore provide a clear illustration of NNs achieving seemingly good performance while remaining completely physics-agnostic, which can be misleading in practice. While this performance comes at the cost of computational complexity, PCNNs on the other hand show accuracy improvements of 17-35% compared to all other physically consistent methods, paving the way for scalable physically consistent models with state-of-the-art performance.

Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state space to find good policies. On the other hand, we postulate that expert knowledge of the system to control often allows us to design simple rules we expect good policies to follow at all times. In this work, we hence propose a simple yet effective modification of continuous actor-critic RL frameworks to incorporate such prior knowledge in the learned policies and constrain them to regions of the state space that are deemed interesting, thereby significantly accelerating their convergence. Concretely, we saturate the actions chosen by the agent if they do not comply with our intuition and, critically, modify the gradient update step of the policy to ensure the learning process does not suffer from the saturation step. On a room temperature control simulation case study, these modifications allow agents to converge to well-performing policies up to one order of magnitude faster than classical RL agents while retaining good final performance.

Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, they might consequently make physically inconsistent predictions, which makes them unreliable in practice. In this paper, we leverage the framework of Irreversible port-Hamiltonian Systems (IPHS), which can describe most multi-physics systems, and rely on Neural Ordinary Differential Equations (NODEs) to learn their parameters from data. Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs). Furthermore, the NODE training procedure allows us to seamlessly incorporate prior knowledge of the system properties in the learned dynamics. We demonstrate the effectiveness of the proposed method by learning the thermodynamics of a building from the real-world measurements and the dynamics of a simulated gas-piston system. Thanks to the modularity and flexibility of the IPHS framework, PC-NODEs can be extended to learn physically consistent models of multi-physics distributed systems.

由于其高能量强度，建筑物在当前全球能源转型中发挥着重要作用。建筑模型是普遍无处不在的，因为在建筑物的每个阶段都需要它们，即设计，改装和控制操作。基于物理方程式的古典白盒式模型必然遵循物理规律，但其底层结构的具体设计可能会阻碍他们的表现力，从而阻碍他们的准确性。另一方面，黑匣子型号更适合捕获非线性建筑动态，因此通常可以实现更好的准确性，但它们需要大量的数据，并且可能不会遵循物理规律，这是神经网络特别常见的问题（NN）模型。为了抵消这种已知的概括问题，最近介绍了物理知识的NNS，研究人员在NNS的结构中介绍了以底层底层物理法律接地，并避免经典的NN概括问题。在这项工作中，我们介绍了一种新的物理信息的NN架构，被称为身体一致的NN（PCNN），其仅需要过去的运行数据并且没有工程开销，包括在并联运行到经典NN的线性模块中的先前知识。我们正式证明，这些网络是物理上一致的 - 通过设计甚至在看不见的数据 - 关于不同的控制输入和邻近区域的温度。我们在案例研究中展示了他们的表现，其中PCNN比3美元的古典物理型电阻电容模型更好地获得高达50美元的准确性。此外，尽管结构受到约束的结构，但PCNNS在验证数据上对古典NNS对古典NNS进行了类似的性能，使训练数据较少，并保留高表达性以解决泛化问题。

Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, Computer Vision may bring substantial improvements when performing an environment-based assistance modulation. In this work, we developed a hip exosuit controller able to distinguish among three different walking terrains through the use of an RGB camera and to adapt the assistance accordingly. The system was tested with seven healthy participants walking throughout an overground path comprising of staircases and level ground. Subjects performed the task with the exosuit disabled (Exo Off), constant assistance profile (Vision Off ), and with assistance modulation (Vision On). Our results showed that the controller was able to promptly classify in real-time the path in front of the user with an overall accuracy per class above the 85%, and to perform assistance modulation accordingly. Evaluation related to the effects on the user showed that Vision On was able to outperform the other two conditions: we obtained significantly higher metabolic savings than Exo Off, with a peak of about -20% when climbing up the staircase and about -16% in the overall path, and than Vision Off when ascending or descending stairs. Such advancements in the field may yield to a step forward for the exploitation of lightweight walking assistive technologies in real-life scenarios.

许多涉及某种形式的3D视觉感知的机器人任务极大地受益于对工作环境的完整知识。但是，机器人通常必须应对非结构化的环境，并且由于工作空间有限，混乱或对象自我划分，它们的车载视觉传感器只能提供不完整的信息。近年来，深度学习架构的形状完成架构已开始将牵引力作为从部分视觉数据中推断出完整的3D对象表示的有效手段。然而，大多数现有的最新方法都以体素电网形式提供了固定的输出分辨率，这与神经网络输出阶段的大小严格相关。尽管这足以完成某些任务，例如导航，抓握和操纵的障碍需要更精细的分辨率，并且简单地扩大神经网络输出在计算上是昂贵的。在本文中，我们通过基于隐式3D表示的对象形状完成方法来解决此限制，该方法为每个重建点提供了置信值。作为第二个贡献，我们提出了一种基于梯度的方法，用于在推理时在任意分辨率下有效地采样这种隐式函数。我们通过将重建的形状与地面真理进行比较，并通过在机器人握把管道中部署形状完成算法来实验验证我们的方法。在这两种情况下，我们将结果与最先进的形状完成方法进行了比较。

动作识别是人形机器人与人类互动和合作的基本能力。该应用程序需要设计动作识别系统，以便可以轻松添加新操作，同时识别和忽略未知的动作。近年来，深度学习的方法代表了行动识别问题的主要解决方案。但是，大多数模型通常需要大量的手动标记样品数据集。在这项工作中，我们针对单发的深度学习模型，因为它们只能处理课堂的一个实例。不幸的是，一击模型假设在推理时，识别的动作落入了支持集中，当动作位于支持集外时，它们会失败。几乎没有射击开放式识别（FSOSR）解决方案试图解决该缺陷，但是当前的解决方案仅考虑静态图像而不是图像序列。静态图像仍然不足以区分诸如坐下和站立之类的动作。在本文中，我们提出了一个新颖的模型，该模型通过一个单发模型来解决FSOSR问题，该模型用拒绝未知动作的歧视器增强。该模型对于人体机器人技术中的应用很有用，因为它允许轻松添加新类并确定输入序列是否是系统已知的序列。我们展示了如何以端到端的方式训练整个模型，并进行定量和定性分析。最后，我们提供现实世界中的例子。

简介：在房颤（AF）导管消融过程（CAP）期间记录了12条铅心电图（ECG）。如果没有长时间的随访评估AF复发（AFR），确定CAP是否成功并不容易。因此，AFR风险预测算法可以使CAP患者更好地管理。在这项研究中，我们从CAP前后记录的12铅ECG中提取功能，并训练AFR风险预测机学习模型。方法：从112例患者中提取前和后段段。该分析包括信号质量标准，心率变异性和由12铅ECG设计的形态生物标志物（总体804个功能）。在112名患者中，有43例AFR临床终点可用。这些用于使用前或后CAP特征来评估AFR风险预测的可行性。在嵌套的交叉验证框架内训练了一个随机的森林分类器。结果：发现36个特征在区分手术前和手术后具有统计学意义（n = 112）。对于分类，报告了接收器操作特性（AUROC）曲线下的区域，AUROC_PRE = 0.64，AUROC_POST = 0.74（n = 43）。讨论和结论：此初步分析表明AFR风险预测的可行性。这样的模型可用于改善盖帽管理。

在随机子集总和问题中，给定$ n $ i.i.d.随机变量$ x_1，...，x_n $，我们希望将[-1,1] $ in [-1,1] $的任何点$ z \作为合适子集的总和$ x_ {i_1（z）}，...，x_ {i_s（z）} $的$，最多$ \ varepsilon $。尽管有简单的陈述，但这个问题还是理论计算机科学和统计力学的基本兴趣。最近，它因其在人工神经网络理论中的影响而引起了人们的重新关注。该问题的一个明显的多维概括是考虑$ n $ i.i.d. \ $ d $ - 二维随机向量，目的是近似于[-1,1]^d $的每个点$ \ Mathbf {z} \。令人惊讶的是，在Lueker的1998年证明，在一维设置中，$ n = o（\ log \ frac 1 \ varepsilon）$ samples $ samples $ samples具有很高可能性的近似属性，在实现上述概括方面几乎没有进展。在这项工作中，我们证明，在$ d $ dimensions中，$ n = o（d^3 \ log \ frac 1 \ varepsilon \ cdot（\ log \ frac 1 \ frac 1 \ varepsilon + log d d））$ samples $ sample近似属性具有很高的概率。作为强调该结果潜在兴趣的应用程序，我们证明了最近提出的神经网络模型表现出\ emph {通用}：具有很高的概率，该模型可以在参数数量中近似多项式开销中的任何神经网络。

执行联合互动需要持续相互监测自己的动作及其对对方行为的影响。这种行动效应的监测受到社会提示的提高，并可能导致越来越多的代理意识。共同行动和联合注意力严格相关，两者都有助于形成精确的时间协调。在人类机器人的互动中，机器人能够与人类伴侣建立共同关注并利用各种社会提示进行反应的能力是创建交流机器人的关键步骤。沿着社会组成部分，可以将有效的人类机器人互动视为改进和使机器人的学习过程更自然和健壮的新方法。在这项工作中，我们使用不同的社交技能，例如相互视线，凝视跟随，言语和人的面部识别，以开发有效的教师学习者场景，适用于动态环境中的视觉对象学习。 ICUB机器人的实验表明，该系统允许机器人通过与人类老师的自然互动来学习新对象，并在存在分心者的情况下学习。