布料的机器人操作的应用包括织物制造业到处理毯子和洗衣。布料操作对于机器人而言是挑战，这主要是由于它们的高度自由度，复杂的动力学和折叠或皱巴巴配置时的严重自我闭合。机器人操作的先前工作主要依赖于视觉传感器，这可能会对细粒度的操纵任务构成挑战，例如从一堆布上抓住所需数量的布料层。在本文中，我们建议将触觉传感用于布操作；我们将触觉传感器（Resin）连接到弗兰卡机器人的两个指尖之一，并训练分类器，以确定机器人是否正在抓住特定数量的布料层。在测试时间实验中，机器人使用此分类器作为其政策的一部分，使用触觉反馈来掌握一两个布层，以确定合适的握把。实验结果超过180次物理试验表明，与使用图像分类器的方法相比，所提出的方法优于不使用触觉反馈并具有更好地看不见布的基准。代码，数据和视频可在https://sites.google.com/view/reskin-cloth上找到。

本文介绍了Deltaz机器人，这是一种厘米级，低成本，三角洲风格的机器人，可提供广泛的功能和鲁棒的功能。当前的技术使三角洲可以通过柔软和刚性材料进行3D印刷，从而易于组装和维护，并降低使用的障碍。机器人的功能源于其三个翻译自由度和一个封闭形式的运动解，这使操作问题与其他操纵器相比更加直观。此外，机器人的低成本为将操纵者民主化为研究环境提供了机会。我们还描述了如何将机器人用作增强学习基准。开源3D打印机设计和代码可向公众使用。

本文介绍了一种新型的分布式灵巧操纵器：三角洲阵列。每个三角洲阵列都由线性驱动的三角形机器人的网格组成，并具有符合性的3D打印的平行四边形链接。这些阵列可用于执行类似于智能输送机的平面运输任务。但是，三角洲的额外自由度也提供了各种不同的平面操作，以及在三角洲集合之间的预感。因此，三角洲阵列提供了广泛的分布式操作策略。在本文中，我们介绍了三角阵列的设计，包括单个三角洲，模块化阵列结构以及分布式通信和控制。我们还使用拟议的设计构建和评估了8x8阵列。我们的评估表明，由此产生的192 DOF机器人能够对各种对象进行各种协调的分布操作，包括翻译，对齐和预性挤压。

For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. Considering the urban scene segmentation scenario, we leverage cheap coarse annotations for real-world captured data, as well as synthetic data to train our model and show competitive performance compared with finely annotated real-world data. Specifically, we propose a coarse-to-fine self-training framework that generates pseudo labels for unlabeled regions of the coarsely annotated data, using synthetic data to improve predictions around the boundaries between semantic classes, and using cross-domain data augmentation to increase diversity. Our extensive experimental results on Cityscapes and BDD100k datasets demonstrate that our method achieves a significantly better performance vs annotation cost tradeoff, yielding a comparable performance to fully annotated data with only a small fraction of the annotation budget. Also, when used as pretraining, our framework performs better compared to the standard fully supervised setting.

A new development in NLP is the construction of hyperbolic word embeddings. As opposed to their Euclidean counterparts, hyperbolic embeddings are represented not by vectors, but by points in hyperbolic space. This makes the most common basic scheme for constructing document representations, namely the averaging of word vectors, meaningless in the hyperbolic setting. We reinterpret the vector mean as the centroid of the points represented by the vectors, and investigate various hyperbolic centroid schemes and their effectiveness at text classification.

Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts. In addition, subsequent fine-tuning can considerably improve performance on a selected downstream task. However, through naive fine-tuning, these zero-shot models lose their generalizability and robustness towards distribution shifts. This is a particular problem for tasks such as Continual Learning (CL), where continuous adaptation has to be performed as new task distributions are introduced sequentially. In this work, we showcase that where fine-tuning falls short to adapt such zero-shot capable models, simple momentum-based weight interpolation can provide consistent improvements for CL tasks in both memory-free and memory-based settings. In particular, we find improvements of over $+4\%$ on standard CL benchmarks, while reducing the error to the upper limit of jointly training on all tasks at once in parts by more than half, allowing the continual learner to inch closer to the joint training limits.

A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a models representations with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumption: that factors are statistically independent. In reality, factors (like object color and shape) are correlated. To address this limitation, we propose a relaxed disentanglement criterion - the Hausdorff Factorized Support (HFS) criterion - that encourages a factorized support, rather than a factorial distribution, by minimizing a Hausdorff distance. This allows for arbitrary distributions of the factors over their support, including correlations between them. We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over +60% in relative improvement over existing disentanglement methods. In addition, we find that leveraging HFS for representation learning can even facilitate transfer to downstream tasks such as classification under distribution shifts. We hope our original approach and positive empirical results inspire further progress on the open problem of robust generalization.

语义图像合成可以通过允许对正在生成的内容进行指导来控制无条件图像的生成。我们从有条件地从预先训练的自动码图像的矢量量化模型（VQ模型）合成潜在空间。我们发现，共同学习调节和图像潜伏期可以显着提高变压器模型的建模能力，而不是在分别学习的条件潜在和图像潜在的潜在的潜在潜在和图像潜伏期上训练自回旋变压器。尽管我们经过训练的VQ模型在语义和图像潜伏期中都达到了类似的重建性能，但在自动编码阶段将两种模式绑定在一起被证明是提高自动性建模性能的重要组成部分。我们表明，我们的模型使用流行的语义图像数据集ADE20K，CityScapes和Coco-stuff上的自回归模型改进语义图像合成。

最先进的深度学习模型通常经过大量昂贵的标签培训数据培训。但是，需要详尽的手动注释可能会降低该模型在有限标签制度中的普遍性。半监督的学习和无监督的学习提供了有希望的范式，可以从大量未标记的视觉数据中学习。这些范式的最新进展表明，利用未标记的数据来改善模型概括并提供更好的模型初始化的良好好处。在这项调查中，我们从统一的角度回顾了有关半监督学习（SSL）和无监督学习（UL）的最新高级深度学习算法（SSL）。为了对这些领域的最先进的整体了解，我们提出了统一的分类法。我们将现有代表性SSL和UL分类为全面而有见地的分析，以在不同的计算机视觉任务中的不同学习场景和应用中突出其设计理由。最后，我们讨论了SSL和UL的新兴趋势和公开挑战，以阐明未来的关键研究方向。

人类在需要快速传达对象信息的游戏中显示出高级的抽象功能。他们将消息内容分解为多个部分，并以可解释的协议将它们传达。为了为机器提供这种功能，我们提出了基于原始的草图抽象任务，其目标是在预算影响下使用一组固定的绘图原始图表示草图。为了解决这项任务，我们的原始匹配网络（PMN）以自我监督的方式学习了草图的可解释抽象。具体而言，PMN将草图的每个笔划都映射到给定集中最相似的原始性，预测了仿射转换将所选原始词与目标冲程对齐的仿射转换。我们学习了端到端的这一笔触至关重要的映射，当原始草图精确地用预测的原语重建时，距离转换损失是最小的。我们的PMN抽象在经验上取得了素描识别和基于草图的图像检索的最高性能，同时也是高度可解释的。这为草图分析打开了新的可能性，例如通过提取定义对象类别的最相关的原始图来比较草图。代码可在https://github.com/explainableml/sketch-primitives上找到。