旨在将原始视觉观察映射到动作的深度视觉运动策略学习在控制任务（例如机器人操纵和自动驾驶）中实现了有希望的结果。但是，它需要与培训环境进行大量在线互动，这限制了其现实世界的应用程序。与流行的无监督功能学习以进行视觉识别相比，探索视觉运动控制任务的功能预读量要少得多。在这项工作中，我们的目标是通过观看长达数小时的未经保育的YouTube视频来预先驾驶任务的政策表示。具体而言，我们使用少量标记数据训练一个反向动态模型，并使用它来预测所有YouTube视频帧的动作标签。然后开发了一种新的对比策略预告片，以从带有伪动作标签的视频框架中学习动作条件的功能。实验表明，由此产生的动作条件特征为下游增强学习和模仿学习任务提供了实质性改进，超出了从以前的无监督学习方法和图预审预周化的体重中预见的重量。代码，模型权重和数据可在以下网址提供：https：//metadriverse.github.io/aco。

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

近年来，预先培训的表述的出现是计算机视觉，自然语言和语音中AI应用的强大抽象。但是，控制策略学习仍然由Tabula-Rasa学习范式主导，而Visuo-Motor策略经常使用部署环境中的数据进行培训。在这种情况下，我们重新审视并研究了预训练的视觉表示对控制的作用，以及在大规模计算机视觉数据集中训练的特定表示。通过对不同控制域（栖息地，深态控制，Adroit，Franka Kitchen）的广泛经验评估，我们隔离和研究了不同表示培训方法，数据增强和功能层次结构的重要性。总体而言，我们发现，预先训练的视觉表示可以比培训控制政策的基本真实状态表示能力更具竞争力甚至更好。尽管仅使用来自标准视觉数据集中的室外数据，但这是没有部署环境中的任何域内数据。源代码以及更多信息，请访问https://sites.google.com/view/pvr-control。

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains. However, deploying visual RL techniques in the real world remains challenging due to their low sample efficiency and large generalization gaps. To tackle these obstacles, data augmentation (DA) has become a widely used technique in visual RL for acquiring sample-efficient and generalizable policies by diversifying the training data. This survey aims to provide a timely and essential review of DA techniques in visual RL in recognition of the thriving development in this field. In particular, we propose a unified framework for analyzing visual RL and understanding the role of DA in it. We then present a principled taxonomy of the existing augmentation techniques used in visual RL and conduct an in-depth discussion on how to better leverage augmented data in different scenarios. Moreover, we report a systematic empirical evaluation of DA-based techniques in visual RL and conclude by highlighting the directions for future research. As the first comprehensive survey of DA in visual RL, this work is expected to offer valuable guidance to this emerging field.

自我监督的方法已通过端到端监督学习的图像分类显着缩小了差距。但是，在人类动作视频的情况下，外观和运动都是变化的重要因素，因此该差距仍然很大。这样做的关键原因之一是，采样对类似的视频剪辑，这是许多自我监督的对比学习方法所需的步骤，目前是保守的，以避免误报。一个典型的假设是，类似剪辑仅在单个视频中暂时关闭，从而导致运动相似性的示例不足。为了减轻这种情况，我们提出了SLIC，这是一种基于聚类的自我监督的对比度学习方法，用于人类动作视频。我们的关键贡献是，我们通过使用迭代聚类来分组类似的视频实例来改善传统的视频内积极采样。这使我们的方法能够利用集群分配中的伪标签来取样更艰难的阳性和负面因素。在UCF101上，SLIC的表现优于最先进的视频检索基线 +15.4％，而直接转移到HMDB51时，SLIC检索基线的率高为15.4％， +5.7％。通过用于动作分类的端到端登录，SLIC在UCF101上获得了83.2％的TOP-1准确性（+0.8％），而HMDB51（+1.6％）上的fric fineTuns in top-1 finetuning。在动力学预处理后，SLIC还与最先进的行动分类竞争。

Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust representation via diversifying the appearances of in-domain observations for better generalization. Limited by the specific observations of the environment, these methods ignore the possibility of exploring diverse real-world image datasets. In this paper, we investigate how a visual RL agent would benefit from the off-the-shelf visual representations. Surprisingly, we find that the early layers in an ImageNet pre-trained ResNet model could provide rather generalizable representations for visual RL. Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner. Extensive experiments are conducted on DMControl Generalization Benchmark, DMControl Manipulation Tasks, Drawer World, and CARLA to verify the effectiveness of PIE-G. Empirical evidence suggests PIE-G improves sample efficiency and significantly outperforms previous state-of-the-art methods in terms of generalization performance. In particular, PIE-G boasts a 55% generalization performance gain on average in the challenging video background setting. Project Page: https://sites.google.com/view/pie-g/home.

我们提出了MACLR，这是一种新颖的方法，可显式执行从视觉和运动方式中学习的跨模式自我监督的视频表示。与以前的视频表示学习方法相比，主要关注学习运动线索的研究方法是隐含的RGB输入，MACLR丰富了RGB视频片段的标准对比度学习目标，具有运动途径和视觉途径之间的跨模式学习目标。我们表明，使用我们的MACLR方法学到的表示形式更多地关注前景运动区域，因此可以更好地推广到下游任务。为了证明这一点，我们在五个数据集上评估了MACLR，以进行动作识别和动作检测，并在所有数据集上展示最先进的自我监督性能。此外，我们表明MACLR表示可以像在UCF101和HMDB51行动识别的全面监督下所学的表示一样有效，甚至超过了对Vidsitu和SSV2的行动识别的监督表示，以及对AVA的动作检测。

跨图像建立视觉对应是一项具有挑战性且必不可少的任务。最近，已经提出了大量的自我监督方法，以更好地学习视觉对应的表示。但是，我们发现这些方法通常无法利用语义信息，并且在低级功能的匹配方面过度融合。相反，人类的视觉能够将不同的物体区分为跟踪的借口。受此范式的启发，我们建议学习语义意识的细粒对应关系。首先，我们证明语义对应是通过一组丰富的图像级别自我监督方法隐式获得的。我们进一步设计了一个像素级的自我监督学习目标，该目标专门针对细粒的对应关系。对于下游任务，我们将这两种互补的对应表示形式融合在一起，表明它们是协同增强性能的。我们的方法超过了先前的最先进的自我监督方法，使用卷积网络在各种视觉通信任务上，包括视频对象分割，人姿势跟踪和人类部分跟踪。

Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and "deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.

在深度学习研究中，自学学习（SSL）引起了极大的关注，引起了计算机视觉和遥感社区的兴趣。尽管计算机视觉取得了很大的成功，但SSL在地球观测领域的大部分潜力仍然锁定。在本文中，我们对在遥感的背景下为计算机视觉的SSL概念和最新发展提供了介绍，并回顾了SSL中的概念和最新发展。此外，我们在流行的遥感数据集上提供了现代SSL算法的初步基准，从而验证了SSL在遥感中的潜力，并提供了有关数据增强的扩展研究。最后，我们确定了SSL未来研究的有希望的方向的地球观察（SSL4EO），以铺平了两个领域的富有成效的相互作用。

鉴于在图像领域的对比学习的成功，目前的自我监督视频表示学习方法通​​常采用对比损失来促进视频表示学习。然而，当空闲地拉动视频的两个增强视图更接近时，该模型倾向于将常见的静态背景作为快捷方式学习但不能捕获运动信息，作为背景偏置的现象。这种偏差使模型遭受弱泛化能力，导致在等下游任务中的性能较差，例如动作识别。为了减轻这种偏见，我们提出\ textbf {f} Oreground-b \ textbf {a} ckground \ textbf {me} rging（sm} rging（fame）故意将所选视频的移动前景区域故意构成到其他人的静态背景上。具体而言，没有任何非货架探测器，我们通过帧差和颜色统计从背景区域中提取移动前景，并在视频中擦拭背景区域。通过利用原始剪辑和熔融夹之间的语义一致性，该模型更多地关注运动模式，并从背景快捷方式中脱位。广泛的实验表明，FAME可以有效地抵抗背景作弊，从而在UCF101，HMDB51和Diving48数据集中实现了最先进的性能。

已经证明了深度增强学习（DRL）对自动驾驶和机器人等几种复杂的决策应用有效。然而，DRL众所周知，众所周知，其高样本复杂性及其缺乏稳定性。先验知识，例如作为专家演示，通常可以提供，但挑战杠杆以减轻这些问题。在本文中，我们提出了一般的增强模仿（GRI），这是一种新的方法，它与勘探和专家数据相结合的好处，并在任何偏离策略的RL算法上实施。我们制作一个简化假设：专家演示可以被视为完美的数据，其基础政策得到了不断的高奖励。基于此假设，GRI介绍了离线演示代理的概念。该代理发送了专家数据，并与来自在线RL探索代理商的经验同时处理。我们表明，我们的方法能够在城市环境中的基于视觉的自主驾驶的重大改进。我们进一步验证了具有不同偏离策略RL算法的Mujoco连续控制任务的GRI方法。我们的方法在Carla排行榜上排名第一，在Rails，以前的最先进，以17％越来越胜过世界。

Developing robots that are capable of many skills and generalization to unseen scenarios requires progress on two fronts: efficient collection of large and diverse datasets, and training of high-capacity policies on the collected data. While large datasets have propelled progress in other fields like computer vision and natural language processing, collecting data of comparable scale is particularly challenging for physical systems like robotics. In this work, we propose a framework to bridge this gap and better scale up robot learning, under the lens of multi-task, multi-scene robot manipulation in kitchen environments. Our framework, named CACTI, has four stages that separately handle data collection, data augmentation, visual representation learning, and imitation policy training. In the CACTI framework, we highlight the benefit of adapting state-of-the-art models for image generation as part of the augmentation stage, and the significant improvement of training efficiency by using pretrained out-of-domain visual representations at the compression stage. Experimentally, we demonstrate that 1) on a real robot setup, CACTI enables efficient training of a single policy capable of 10 manipulation tasks involving kitchen objects, and robust to varying layouts of distractor objects; 2) in a simulated kitchen environment, CACTI trains a single policy on 18 semantic tasks across up to 50 layout variations per task. The simulation task benchmark and augmented datasets in both real and simulated environments will be released to facilitate future research.

We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial. We carefully design data augmentations involving spatial and temporal cues. Concretely, we propose a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. We also propose a sampling-based temporal augmentation method to avoid overly enforcing invariance on clips that are distant in time. On Kinetics-600, a linear classifier trained on the representations learned by CVRL achieves 70.4% top-1 accuracy with a 3D-ResNet-50 (R3D-50) backbone, outperforming ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8% using the same inflated R3D-50. The performance of CVRL can be further improved to 72.9% with a larger R3D-152 (2× filters) backbone, significantly closing the gap between unsupervised and supervised video representation learning. Our code and models will be available at https://github.com/tensorflow/models/tree/master/official/.

最近，自我监督的表示学习（SSRL）在计算机视觉，语音，自然语言处理（NLP）以及最近的其他类型的模式（包括传感器的时间序列）中引起了很多关注。自我监督学习的普及是由传统模型通常需要大量通知数据进行培训的事实所驱动的。获取带注释的数据可能是一个困难且昂贵的过程。已经引入了自我监督的方法，以通过使用从原始数据自由获得的监督信号对模型进行判别预训练来提高训练数据的效率。与现有的对SSRL的评论不同，该评论旨在以单一模式为重点介绍CV或NLP领域的方法，我们旨在为时间数据提供对多模式自我监督学习方法的首次全面审查。为此，我们1）提供现有SSRL方法的全面分类，2）通过定义SSRL框架的关键组件来引入通用管道，3）根据其目标功能，网络架构和潜在应用程序，潜在的应用程序，潜在的应用程序，比较现有模型， 4）查看每个类别和各种方式中的现有多模式技术。最后，我们提出了现有的弱点和未来的机会。我们认为，我们的工作对使用多模式和/或时间数据的域中SSRL的要求有了一个观点

模仿学习研究社区最近取得了重大进展，以使人工代理人仅凭视频演示模仿行为。然而，由于视频观察的高维质性质，针对此问题开发的当前最新方法表现出很高的样本复杂性。为了解决这个问题，我们在这里介绍了一种新的算法，称为使用状态观察者VGAIFO-SO从观察中获得的，称为视觉生成对抗性模仿。 Vgaifo-So以此为核心，试图使用一种新型的自我监管的状态观察者来解决样本效率低下，该观察者从高维图像中提供了较低维度的本体感受状态表示的估计。我们在几个连续的控制环境中进行了实验表明，Vgaifo-SO比其他IFO算法更有效地从仅视频演示中学习，有时甚至可以实现与观察（Gaifo）算法的生成对抗性模仿（Gaifo）算法的性能，该算法有特权访问访问权限示威者的本体感知状态信息。

无监督的表示学习的最新进展显着提高了模拟环境中培训强化学习政策的样本效率。但是，尚未看到针对实体强化学习的类似收益。在这项工作中，我们专注于从像素中启用数据有效的实体机器人学习。我们提出了有效的机器人学习（编码器）的对比前训练和数据增强，该方法利用数据增强和无监督的学习来从稀疏奖励中实现对实体ARM策略的样本效率培训。虽然对比预训练，数据增强，演示和强化学习不足以进行有效学习，但我们的主要贡献表明，这些不同技术的组合导致了一种简单而数据效率的方法。我们表明，只有10个示范，一个机器人手臂可以从像素中学习稀疏的奖励操纵策略，例如到达，拾取，移动，拉动大物体，翻转开关并在短短30分钟内打开抽屉现实世界训练时间。我们在项目网站上包括视频和代码：https：//sites.google.com/view/felfficited-robotic-manipulation/home

The objective of this paper is visual-only self-supervised video representation learning. We make the following contributions: (i) we investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (In-foNCE) training, showing that this form of supervised contrastive learning leads to a clear improvement in performance; (ii) we propose a novel self-supervised co-training scheme to improve the popular infoNCE loss, exploiting the complementary information from different views, RGB streams and optical flow, of the same data source by using one view to obtain positive class samples for the other; (iii) we thoroughly evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval. In both cases, the proposed approach demonstrates state-of-the-art or comparable performance with other self-supervised approaches, whilst being significantly more efficient to train, i.e. requiring far less training data to achieve similar performance.

对比学习在视频表示学习中表现出了巨大的潜力。但是，现有方法无法充分利用短期运动动态，这对于各种下游视频理解任务至关重要。在本文中，我们提出了运动敏感的对比度学习（MSCL），该学习将光学流捕获的运动信息注入RGB帧中，以增强功能学习。为了实现这一目标，除了剪辑级全球对比度学习外，我们还开发了局部运动对比度学习（LMCL），具有两种模式的框架级对比目标。此外，我们引入流动旋转增强（FRA），以生成额外的运动除件负面样品和运动差分采样（MDS）以准确筛选训练样品。对标准基准测试的广泛实验验证了该方法的有效性。以常用的3D RESNET-18为骨干，我们在UCF101上获得了91.5 \％的前1个精度，而在视频分类中进行了一些v2的v2，以及65.6 \％的top-1 top-1召回ucf1011对于视频检索，特别是改善了最新的。

We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs offpolicy control on top of the extracted features. CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind Control Suite and Atari Games showing 1.9x and 1.2x performance gains at the 100K environment and interaction steps benchmarks respectively. On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features. Our code is open-sourced and available at https://www. github.com/MishaLaskin/curl.