对于诸如搜索和救援之类的苛刻情况下，人形生物的部署，高度智能的决策和熟练的感觉运动技能。一个有前途的解决方案是通过远程操作通过互连机器人和人类来利用人类的实力。为了创建无缝的操作，本文提出了一个动态的远程组分框架，该框架将人类飞行员的步态与双皮亚机器人的步行同步。首先，我们介绍了一种方法，以从人类飞行员的垫脚行为中生成虚拟人类步行模型，该模型是机器人行走的参考。其次，步行参考和机器人行走的动力学通过向人类飞行员和机器人施加力来同步，以实现两个系统之间的动态相似性。这使得人类飞行员能够不断感知并取消步行参考和机器人之间的任何异步。得出机器人的一致步骤放置策略是通过步骤过渡来维持动态相似性的。使用我们的人机界面，我们证明了人类飞行员可以通过地位，步行和干扰拒绝实验实现模拟机器人的稳定和同步近距离运行。这项工作为将人类智力和反射转移到人形机器人方面提供了基本的一步。

Teleperation已成为全自动系统，以实现人类机器人的人体水平能力的替代解决方案。具体而言，全身控制的远程运行是指挥类人动物的有前途的无提手术策略，但需要更多的身体和心理努力。为了减轻这一限制，研究人员提出了共享控制方法，结合了机器人决策，以帮助人类完成低级任务，从而进一步减少了运营工作。然而，尚未探索用于全身级别的人型类人形端粒体的共享控制方法。在这项工作中，我们研究了全身反馈如何影响不同环境中不同共享控制方法的性能。提出了时间衍生的Sigmoid功能（TDSF），以产生障碍物的更直观的力反馈。进行了全面的人类实验，结果得出的结论是，力反馈增强了在不熟悉的环境中的全身端粒化表现，但可以在熟悉的环境中降低性能。通过触觉传达机器人的意图显示出进一步的改进，因为操作员可以将力反馈用于短途计划和视觉反馈进行长距离计划。

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.

Multi-object state estimation is a fundamental problem for robotic applications where a robot must interact with other moving objects. Typically, other objects' relevant state features are not directly observable, and must instead be inferred from observations. Particle filtering can perform such inference given approximate transition and observation models. However, these models are often unknown a priori, yielding a difficult parameter estimation problem since observations jointly carry transition and observation noise. In this work, we consider learning maximum-likelihood parameters using particle methods. Recent methods addressing this problem typically differentiate through time in a particle filter, which requires workarounds to the non-differentiable resampling step, that yield biased or high variance gradient estimates. By contrast, we exploit Fisher's identity to obtain a particle-based approximation of the score function (the gradient of the log likelihood) that yields a low variance estimate while only requiring stepwise differentiation through the transition and observation models. We apply our method to real data collected from autonomous vehicles (AVs) and show that it learns better models than existing techniques and is more stable in training, yielding an effective smoother for tracking the trajectories of vehicles around an AV.

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.

This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior. A step for computing the predictive distribution, involving convolution, is computed via either numerical integration, or via fast transform methods.

Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid laws). To resolve this conundrum, we propose a "light touch" approach, guiding visual Transformers to learn multiple-view geometry but allowing them to break free when needed. We achieve this by using epipolar lines to guide the Transformer's cross-attention maps, penalizing attention values outside the epipolar lines and encouraging higher attention along these lines since they contain geometrically plausible matches. Unlike previous methods, our proposal does not require any camera pose information at test-time. We focus on pose-invariant object instance retrieval, where standard Transformer networks struggle, due to the large differences in viewpoint between query and retrieved images. Experimentally, our method outperforms state-of-the-art approaches at object retrieval, without needing pose information at test-time.

Graph Neural Networks (GNNs) have achieved great successes in many learning tasks performed on graph structures. Nonetheless, to propagate information GNNs rely on a message passing scheme which can become prohibitively expensive when working with industrial-scale graphs. Inspired by the PPRGo model, we propose the CorePPR model, a scalable solution that utilises a learnable convex combination of the approximate personalised PageRank and the CoreRank to diffuse multi-hop neighbourhood information in GNNs. Additionally, we incorporate a dynamic mechanism to select the most influential neighbours for a particular node which reduces training time while preserving the performance of the model. Overall, we demonstrate that CorePPR outperforms PPRGo, particularly on large graphs where selecting the most influential nodes is particularly relevant for scalability. Our code is publicly available at: https://github.com/arielramos97/CorePPR.

贝叶斯优化（BO）算法在涉及昂贵的黑盒功能的应用中表现出了显着的成功。传统上，BO被设置为一个顺序决策过程，该过程通过采集函数和先前的功能（例如高斯过程）来估计查询点的实用性。然而，最近，通过密度比率估计（BORE）对BO进行重新制定允许将采集函数重新诠释为概率二进制分类器，从而消除了对函数的显式先验和提高可伸缩性的需求。在本文中，我们介绍了对孔的遗憾和算法扩展的理论分析，并提高了不确定性估计。我们还表明，通过将问题重新提交为近似贝叶斯推断，可以自然地扩展到批处理优化设置。所得算法配备了理论性能保证，并在一系列实验中对其他批处理基本线进行了评估。

序数模式的统计分析的最终目的是表征它们诱导的特征的分布。特别是，了解大类时间序列模型的对熵统计复杂性的联合分布将允许迄今无法获得的统计测试。在这个方向上工作，我们表征了Shannon经验的渐进分布，用于任何模型，在此模型中，真正的归一化熵既不为零也不为零。我们从中心极限定理（假设大时间序列），多元增量方法和其平均值的三阶校正获得了渐近分布。我们讨论了其他结果（精确，一阶和二阶校正）有关其准确性和数值稳定性的适用性。在建立有关香农熵的测试统计数据的一般框架内，我们提出了双边测试，该测试验证是否有足够的证据拒绝以下假设，即两个信号产生了具有相同Shannon熵的顺序模式。我们将此双边测试应用于来自三个城市（都柏林，爱丁堡和迈阿密）的每日最高温度时间序列，并获得了明智的结果。