同时本地化和映射（SLAM）是自动移动机器人中的基本问题之一，在该机器人需要重建以前看不见的环境的同时，同时在地图上进行了本身。特别是，Visual-Slam使用移动机器人中的各种传感器来收集和感测地图的表示。传统上，基于几何模型的技术被用来解决大满贯问题，在充满挑战的环境下，该问题往往容易出错。诸如深度学习技术之类的计算机视觉方面的最新进展提供了一种数据驱动的方法来解决视觉范围问题。这篇综述总结了使用各种基于学习的方法的视觉 - 峰领域的最新进展。我们首先提供了基于几何模型的方法的简洁概述，然后进行有关SLAM当前范式的技术评论。然后，我们介绍了从移动机器人那里收集感官输入并执行场景理解的各种基于学习的方法。讨论并将基于深度学习的语义理解中的当前范式讨论并置于视觉峰的背景下。最后，我们讨论了在视觉 - 峰中基于学习的方法方向上的挑战和进一步的机会。

我们提出了一种新颖的方法，用于计划代理人通过观察和学习与世界的历史互动来构成抽象技能。我们的框架通过在未知的前条件下通过一组动作在马尔可夫州空间模型中运行。我们将技能制定为高级抽象政策，该政策根据当前状态提出行动计划。每个政策通过观察各州的过渡来学习新计划，而代理商与世界互动。这种方法会自动学习新的计划以实现特定的预期效果，但是这种计划的成功通常取决于它们适用的状态。因此，我们制定了对无限多部多臂匪徒问题等计划的评估，在这些计划中，我们在评估现有武器的成功概率和探索新选项的成功概率上平衡了资源的分配。结果是一个计划者，能够在嘈杂的环境下自动学习强大的高级技能。这样的技能隐含地学习了没有明确知识的行动前提。我们表明，这种计划方法在高维状态空间域中在实验上非常有竞争力。

信息爆炸的时代促使累积巨大的时间序列数据，包括静止和非静止时间序列数据。最先进的算法在处理静止时间数据方面取得了体面的性能。然而，解决静止​​时间系列的传统算法不适用于外汇交易的非静止系列。本文调查了适用的模型，可以提高预测未来非静止时间序列序列趋势的准确性。特别是，我们专注于识别潜在模型，并调查识别模式从历史数据的影响。我们提出了基于RNN的\ Rebuttal {The} SEQ2Seq模型的组合，以及通过动态时间翘曲和Zigzag峰谷指示器提取的注重机制和富集的集合特征。定制损失函数和评估指标旨在更加关注预测序列的峰值和谷点。我们的研究结果表明，我们的模型可以在外汇数据集中预测高精度的4小时未来趋势，这在逼真的情况下至关重要，以协助外汇交易决策。我们进一步提供了对各种损失函数，评估指标，模型变体和组件对模型性能的影响的评估。

在操纵器需要执行多个连续任务的环境中，对象操纵的行为将改变基础配置空间，从而影响所有后续任务。以前的免费配置现在可能被操纵的对象占据，并且以前占用的空间现在可能打开新的路径。我们提出了基于懒惰的Replanner（LTR*） - 一种新型的混合计划者，旨在继承现有的任何时间增量采样计划者的快速计划性质。同时，它允许后续任务通过懒惰的体验图来利用先前的经验。以懒惰的图结构总结了先前的经验，而Ltr*的表述是强大和有益的，无论工作空间的变化程度如何。与现有的基于路线图的规划人员相比，我们的混合方法在获得初始解决方案方面的速度更快，并且轨迹长度的成本较低。随后的任务可以利用懒惰的体验图来加快找到解决方案并利用优化图来最大程度地减少成本目标。我们提供概率完整性和几乎渐近的最佳保证的证明。在实验上，我们表明，在重复的选择任务中，LTR*在计划后续任务时的性能很高。

In this paper, we propose a novel framework dubbed peer learning to deal with the problem of biased scene graph generation (SGG). This framework uses predicate sampling and consensus voting (PSCV) to encourage different peers to learn from each other, improving model diversity and mitigating bias in SGG. To address the heavily long-tailed distribution of predicate classes, we propose to use predicate sampling to divide and conquer this issue. As a result, the model is less biased and makes more balanced predicate predictions. Specifically, one peer may not be sufficiently diverse to discriminate between different levels of predicate distributions. Therefore, we sample the data distribution based on frequency of predicates into sub-distributions, selecting head, body, and tail classes to combine and feed to different peers as complementary predicate knowledge during the training process. The complementary predicate knowledge of these peers is then ensembled utilizing a consensus voting strategy, which simulates a civilized voting process in our society that emphasizes the majority opinion and diminishes the minority opinion. This approach ensures that the learned representations of each peer are optimally adapted to the various data distributions. Extensive experiments on the Visual Genome dataset demonstrate that PSCV outperforms previous methods. We have established a new state-of-the-art (SOTA) on the SGCls task by achieving a mean of \textbf{31.6}.

Audio-Visual scene understanding is a challenging problem due to the unstructured spatial-temporal relations that exist in the audio signals and spatial layouts of different objects and various texture patterns in the visual images. Recently, many studies have focused on abstracting features from convolutional neural networks while the learning of explicit semantically relevant frames of sound signals and visual images has been overlooked. To this end, we present an end-to-end framework, namely attentional graph convolutional network (AGCN), for structure-aware audio-visual scene representation. First, the spectrogram of sound and input image is processed by a backbone network for feature extraction. Then, to build multi-scale hierarchical information of input features, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network. Notably, to well represent the salient regions and contextual information of audio-visual inputs, the salient acoustic graph (SAG) and contextual acoustic graph (CAG), salient visual graph (SVG), and contextual visual graph (CVG) are constructed for the audio-visual scene representation. Finally, the constructed graphs pass through a graph convolutional network for structure-aware audio-visual scene recognition. Extensive experimental results on the audio, visual and audio-visual scene recognition datasets show that promising results have been achieved by the AGCN methods. Visualizing graphs on the spectrograms and images have been presented to show the effectiveness of proposed CAG/SAG and CVG/SVG that could focus on the salient and semantic relevant regions.

With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic gradient and establish the asymptotic normality of the parameter estimator. Our proposed estimator significantly improves the asymptotic efficiency over the previous averaged SGD approach via inverse probability weights. We also conduct an optimality analysis on the weights in a linear regression setting. We provide a Bahadur representation of the proposed estimator and show that the remainder term in the Bahadur representation entails a slower convergence rate compared to classical SGD due to the adaptive data collection.

Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model counting, approximate techniques have been developed to solve real-world instances of model counting. This paper designs a new anytime approach called PartialKC for approximate model counting. The idea is a form of partial knowledge compilation to provide an unbiased estimate of the model count which can converge to the exact count. Our empirical analysis demonstrates that PartialKC achieves significant scalability and accuracy over prior state-of-the-art approximate counters, including satss and STS. Interestingly, the empirical results show that PartialKC reaches convergence for many instances and therefore provides exact model counting performance comparable to state-of-the-art exact counters.

Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex interplay between the controller and morphology. In this paper, we propose a learning-based control method that can inherently take morphology into consideration such that once the control policy is trained in the simulator, it can be easily deployed to robots with different embodiments in the real world. In particular, we present the Embodiment-aware Transformer (EAT), an architecture that casts this control problem as conditional sequence modeling. EAT outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired robot embodiment, past states, and actions, our EAT model can generate future actions that best fit the current robot embodiment. Experimental results show that EAT can outperform all other alternatives in embodiment-varying tasks, and succeed in an example of real-world evolution tasks: stepping down a stair through updating the morphology alone. We hope that EAT will inspire a new push toward real-world evolution across many domains, where algorithms like EAT can blaze a trail by bridging the field of evolutionary robotics and big data sequence modeling.

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.