本文解决了任务执行过程中远程移动机器人中自动检测和量化性能退化的问题。在执行任务期间，机器人可能会遇到各种不确定性和逆境，这可能会损害其有效执行任务并导致其绩效降级的能力。可以通过及时的检测和干预来缓解或避免这种情况（例如，由远程人类主管接管在远程操作模式下的控制）。受到医院中患者分类系统的启发，我们介绍了“机器人生命力”的框架，以估算整体“机器人健康”。机器人的生命值是一组指标，可以估计机器人在给定时间点面临的性能降解程度。机器人健康是一种将机器人生命力结合到性能降解的单个标量值估计值中的度量。在模拟和实际移动机器人中，实验表明，可以有效地使用提出的机器人生命力和机器人健康来估计运行时机器人性能降解。

本文报道了机器人研究人员的见解，该洞察力参加了由德国卡尔斯鲁赫（Karlsruhe）的Kerntechnische Hilfdienst GmbH（KHG）进行的为期5天的核灾难反应现场演习。德国核工业建立了KHG，为核事故提供了机器人辅助的紧急响应能力。我们对所使用的设备进行系统描述；机器人操作员的培训计划；现场锻炼和机器人任务；练习期间遵循的协议。此外，我们还提供了基于这些观察结果来推进灾难响应机器人技术的见解和建议。具体而言，性能的主要退化来自对操作员的认知和注意力需求。此外，除了易用性外，机器人平台和模块还应旨在保持健壮和可靠。最后，由于紧急响应利益相关者通常对使用自主系统持怀疑态度，因此我们建议采用可变的自主权范式将自主机器人的能力与人类的自主机器人能力逐渐融合在一起。远程操作和自主权之间的这种中间立场可以增加最终用户的接受，同时直接减轻操作员的机器人控制负担并保持人类陆路的弹性。

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022.

人类对象与铰接物体的相互作用在日常生活中很普遍。尽管单视图3D重建方面取得了很多进展，但从RGB视频中推断出一个铰接的3D对象模型仍然具有挑战性，显示一个人操纵对象的人。我们从RGB视频中划定了铰接的3D人体对象相互作用重建的任务，并对这项任务进行了五个方法家族的系统基准：3D平面估计，3D Cuboid估计，CAD模型拟合，隐式现场拟合以及自由 - 自由 - 形式网状配件。我们的实验表明，即使提供了有关观察到的对象的地面真相信息，所有方法也难以获得高精度结果。我们确定使任务具有挑战性的关键因素，并为这项具有挑战性的3D计算机视觉任务提出指示。短视频摘要https://www.youtube.com/watch?v=5talkbojzwc

截断的线性回归是统计学中的一个经典挑战，其中$ y = w^t x + \ varepsilon $及其相应的功能向量，$ x \ in \ mathbb {r}^k $，仅在当时才观察到标签属于某些子集$ s \ subseteq \ mathbb {r} $;否则，对$（x，y）$的存在被隐藏在观察中。以截断的观察结果的线性回归一直是其一般形式的挑战，因为〜\ citet {tobin1958估计，amemiya1973 reflecression}的早期作品。当误差的分布与已知方差正常时，〜\ citet {daskalakis2019 truncatedRegerse}的最新工作在线性模型$ w $上提供了计算和统计上有效的估计器。在本文中，当噪声方差未知时，我们为截断的线性回归提供了第一个计算和统计上有效的估计器，同时估计了噪声的线性模型和方差。我们的估计器基于对截短样品的负模样中的预测随机梯度下降的有效实施。重要的是，我们表明我们的估计错误是渐近正常的，我们使用它来为我们的估计提供明确的置信区域。

随着中间级表示桥接这两个级别，视觉场景的结构化表示，例如成对对象之间的视觉关系，不仅可以使组成模型与结构进行推理，而且为模型决策提供了更高的解释性。然而，这些表示的关注要比传统的认可任务少得多，因此尚未解决许多公开挑战。在论文中，我们研究了机器如何以视觉关系为结构化表示的单个图像或视频的内容。具体而言，我们探讨了如何在静态图像和视频设置中有效地构建和学习的视觉场景的结构化表示，以及由外部知识融合，减少偏置机制以及增强的表示模型所带来的改进。在本论文的结尾，我们还讨论了一些开放的挑战和局限性，以阐明视觉场景的结构化表示学习的未来方向。

In the classical setting of self-selection, the goal is to learn $k$ models, simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the output of one of $k$ underlying models on input $x^{(i)}$. In contrast to mixture models, where we observe the output of a randomly selected model, here the observed model depends on the outputs themselves, and is determined by some known selection criterion. For example, we might observe the highest output, the smallest output, or the median output of the $k$ models. In known-index self-selection, the identity of the observed model output is observable; in unknown-index self-selection, it is not. Self-selection has a long history in Econometrics and applications in various theoretical and applied fields, including treatment effect estimation, imitation learning, learning from strategically reported data, and learning from markets at disequilibrium. In this work, we present the first computationally and statistically efficient estimation algorithms for the most standard setting of this problem where the models are linear. In the known-index case, we require poly$(1/\varepsilon, k, d)$ sample and time complexity to estimate all model parameters to accuracy $\varepsilon$ in $d$ dimensions, and can accommodate quite general selection criteria. In the more challenging unknown-index case, even the identifiability of the linear models (from infinitely many samples) was not known. We show three results in this case for the commonly studied $\max$ self-selection criterion: (1) we show that the linear models are indeed identifiable, (2) for general $k$ we provide an algorithm with poly$(d) \exp(\text{poly}(k))$ sample and time complexity to estimate the regression parameters up to error $1/\text{poly}(k)$, and (3) for $k = 2$ we provide an algorithm for any error $\varepsilon$ and poly$(d, 1/\varepsilon)$ sample and time complexity.

几种广泛使用的一阶马鞍点优化方法将衍生天然衍生时的梯度下降成本（GDA）方法的相同连续时间常分等式（ODE）。然而，即使在简单的双线性游戏上，它们的收敛性也很差异。我们使用一种来自流体动力学的技术，称为高分辨率微分方程（HRDE）来设计几个骑马点优化方法的杂散。在双线性游戏中，派生HRDE的收敛性属性对应于起始离散方法的收敛性。使用这些技术，我们表明乐观梯度下降的HRDE具有最后迭代单调变分不等式的迭代收敛。据我们所知，这是第一个连续时间动态，用于收敛此类常规设置。此外，我们提供了ogda方法的最佳迭代收敛的速率，仅依靠单调运营商的一阶平滑度。