While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io

尽管行为学习近期取得了令人印象深刻的进步，但由于无法利用大型，人类生成的数据集，它落后于计算机视觉和自然语言处理。人类的行为具有较大的差异，多种模式和人类的示范通常不带有奖励标签。这些属性限制了当前方法在离线RL和行为克隆中的适用性，以从大型预收取的数据集中学习。在这项工作中，我们提出了行为变压器（BET），这是一种用多种模式建模未标记的演示数据的新技术。 BET翻新带有动作离散化的标准变压器体系结构，再加上受对象检测中偏移预测启发的多任务动作校正。这使我们能够利用现代变压器的多模式建模能力来预测多模式的连续动作。我们通过实验评估了各种机器人操作和自动驾驶行为数据集的赌注。我们表明，BET可以显着改善以前的最新工作解决方案，同时捕获预采用的数据集中存在的主要模式。最后，通过一项广泛的消融研究，我们分析了BET中每个关键成分的重要性。 BET生成的行为视频可在https://notmahi.github.io/bet上获得

长摩根和包括一系列隐性子任务的日常任务仍然在离线机器人控制中构成了重大挑战。尽管许多先前的方法旨在通过模仿和离线增强学习的变体来解决这种设置，但学习的行为通常是狭窄的，并且经常努力实现可配置的长匹配目标。由于这两个范式都具有互补的优势和劣势，因此我们提出了一种新型的层次结构方法，结合了两种方法的优势，以从高维相机观察中学习任务无关的长胜压策略。具体而言，我们结合了一项低级政策，该政策通过模仿学习和从离线强化学习中学到的高级政策学习潜在的技能，以促进潜在的行为先验。各种模拟和真实机器人控制任务的实验表明，我们的配方使以前看不见的技能组合能够通过“缝制”潜在技能通过目标链条，并在绩效上提高绩效的顺序，从而实现潜在的目标。艺术基线。我们甚至还学习了一个多任务视觉运动策略，用于现实世界中25个不同的操纵任务，这既优于模仿学习和离线强化学习技术。

强化学习（RL）算法有望为机器人系统实现自主技能获取。但是，实际上，现实世界中的机器人RL通常需要耗时的数据收集和频繁的人类干预来重置环境。此外，当部署超出知识的设置超出其学习的设置时，使用RL学到的机器人政策通常会失败。在这项工作中，我们研究了如何通过从先前看到的任务中收集的各种离线数据集的有效利用来应对这些挑战。当面对一项新任务时，我们的系统会适应以前学习的技能，以快速学习执行新任务并将环境返回到初始状态，从而有效地执行自己的环境重置。我们的经验结果表明，将先前的数据纳入机器人增强学习中可以实现自主学习，从而大大提高了学习的样本效率，并可以更好地概括。

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.

虽然视觉模仿学习提供了从视觉演示中学习最有效的方法之一，但从它们中概括需要数百个不同的演示，任务特定的前瞻或大型难以列车的参数模型。此类复杂性出现的一个原因是因为标准的视觉模仿框架尝试一次解决两个耦合问题：从不同的视觉数据中学习简洁但良好的表示，同时学习将显示的动作与这样的表示相关联。这种联合学习导致这两个问题之间的相互依存，这通常会导致需要大量的学习演示。为了解决这一挑战，我们建议与对视觉模仿的行为学习的表现脱钩。首先，我们使用标准监督和自我监督的学习方法从离线数据中学习视觉表示编码器。培训表示，我们使用非参数局部加权回归来预测动作。我们通过实验表明，与目视模仿的先前工作相比，这种简单的去耦可提高离线演示数据集和实际机器人门开口的视觉模仿模型的性能。我们所有生成的数据，代码和机器人视频都在https://jyopari.github.io/vinn/处公开提供。

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

最近的工作表明，单独监督学习，没有时间差异（TD）学习，可以对离线RL显着有效。什么时候保持真实，需要哪些算法组件？通过广泛的实验，我们致力于将RL离线的监督学习到其基本要素。在我们考虑的每个环境套件中，只需通过双层前馈MLP最大化的可能性，与基于TD学习或与变压器的序列建模的基本更复杂的方法具有竞争力的竞争性。仔细选择模型容量（例如，通过正则化或架构），并选择哪些信息（例如，目标或奖励）对性能至关重要。这些见解是通过监督学习进行加强学习的从业者（我们投入“RVS学习”）的实践指南。他们还探讨了现有RVS方法的限制，在随机数据上相对较弱，并提出了许多打开问题。

与人类在环境中共存的通用机器人必须学会将人类语言与其在一系列日常任务中有用的看法和行动联系起来。此外，他们需要获取各种曲目的一般专用技能，允许通过遵循无约束语言指示来组成长地平任务。在本文中，我们呈现了凯文（从语言和愿景撰写的行动），是一个露天模拟基准，用于学习Long-Horizo​​ n语言条件的任务。我们的目的是使可以开发能够通过船上传感器解决许多机器人操纵任务的代理商，并且仅通过人类语言指定。 Calvin任务在序列长度，动作空间和语言方面更复杂，而不是现有的视觉和语言任务数据集，并支持灵活的传感器套件规范。我们评估零拍摄的代理商以新颖的语言指示以及新的环境和对象。我们表明，基于多语境模仿学习的基线模型在凯文中表现不佳，表明有很大的空间，用于开发创新代理，了解学习将人类语言与这款基准相关的世界模型。

我们调查视觉跨实施的模仿设置，其中代理商学习来自其他代理的视频（例如人类）的策略，示范相同的任务，但在其实施例中具有缺点差异 - 形状，动作，终效应器动态等。在这项工作中，我们证明可以从对这些差异强大的跨实施例证视频自动发现和学习基于视觉的奖励功能。具体而言，我们介绍了一种用于跨实施的跨实施的自我监督方法（XIRL），它利用时间周期 - 一致性约束来学习深度视觉嵌入，从而从多个专家代理的示范的脱机视频中捕获任务进度，每个都执行相同的任务不同的原因是实施例差异。在我们的工作之前，从自我监督嵌入产生奖励通常需要与参考轨迹对齐，这可能难以根据STARK实施例的差异来获取。我们凭经验显示，如果嵌入式了解任务进度，则只需在学习的嵌入空间中占据当前状态和目标状态之间的负距离是有用的，作为培训与加强学习的培训政策的奖励。我们发现我们的学习奖励功能不仅适用于在训练期间看到的实施例，而且还概括为完全新的实施例。此外，在将现实世界的人类示范转移到模拟机器人时，我们发现XIRL比当前最佳方法更具样本。 https://x-irl.github.io提供定性结果，代码和数据集

在机器人技术中，以可扩展的方式构建各种操纵技巧的曲目仍然是一个未解决的挑战。解决这一挑战的一种方法是在非结构化的人类游戏中，人类在环境中自由运作以实现未指定的目标。游戏是一种简单且廉价的方法，用于收集各种用户演示，并在环境中进行广泛的状态和目标覆盖。由于这种不同的覆盖范围，现有的从游戏中学习的方法对离线数据分布的在线政策偏差更加牢固。但是，这些方法通常很难在场景变化和具有挑战性的操纵基础上学习，部分原因是将复杂的行为与他们引起的场景变化联系起来。我们的见解是，以对象数据为中心的观点可以帮助将人类的行为和所产生的环境变化联系起来，从而改善多任务策略学习。在这项工作中，我们构建了一个潜在空间来建模对象\ textit {proffances} - 在环境中定义其用途的对象的属性，然后学习实现所需负担的策略。通过对可变范围任务进行建模和预测所需的负担，我们的方法通过以对象为中心的游戏（PLATO）预测潜在的负担，在2D和3D对象操纵模拟和现实世界环境中，在复杂的操纵任务上的现有方法优于现有方法互动。可以在我们的网站上找到视频：https：//tinyurl.com/4U23HWFV

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised "practice" phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method. Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques. * Equal contribution. Order was determined by coin flip.

将监督学习的力量（SL）用于更有效的强化学习（RL）方法，这是最近的趋势。我们通过交替在线RL和离线SL来解决稀疏奖励目标条件问题，提出一种新颖的阶段方法。在在线阶段，我们在离线阶段进行RL培训并收集推出数据，我们对数据集的这些成功轨迹执行SL。为了进一步提高样本效率，我们在在线阶段采用其他技术，包括减少任务以产生更可行的轨迹和基于价值的基于价值的内在奖励，以减轻稀疏的回报问题。我们称此总体算法为阶段性的自我模拟还原（Pair）。对稀疏的奖励目标机器人控制问题（包括具有挑战性的堆叠任务），对基本上优于非强调RL和Phasic SL基线。 Pair是第一个学习堆叠6个立方体的RL方法，只有0/1成功从头开始奖励。

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods can directly address the problem of sequential decision-making. We view decision-making not through the lens of reinforcement learning (RL), but rather through conditional generative modeling. To our surprise, we find that our formulation leads to policies that can outperform existing offline RL approaches across standard benchmarks. By modeling a policy as a return-conditional diffusion model, we illustrate how we may circumvent the need for dynamic programming and subsequently eliminate many of the complexities that come with traditional offline RL. We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills. Conditioning on a single constraint or skill during training leads to behaviors at test-time that can satisfy several constraints together or demonstrate a composition of skills. Our results illustrate that conditional generative modeling is a powerful tool for decision-making.

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.

Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-motor control. Model-based RL has the potential to be highly sample efficient by concurrently learning a world model and using synthetic rollouts for planning and policy improvement. However, in practice, sample-efficient learning with model-based RL is bottlenecked by the exploration challenge. In this work, we find that leveraging just a handful of demonstrations can dramatically improve the sample-efficiency of model-based RL. Simply appending demonstrations to the interaction dataset, however, does not suffice. We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework. We empirically study three complex visuo-motor control domains and find that our method is 150%-250% more successful in completing sparse reward tasks compared to prior approaches in the low data regime (100K interaction steps, 5 demonstrations). Code and videos are available at: https://nicklashansen.github.io/modemrl

我们通过在野外观看人类来解决学习问题。尽管在现实世界中学习的传统方法和强化学习对于学习是有希望的，但它们要么是效率低下的样本，要么被限制在实验室环境中。同时，处理被动的，非结构化的人类数据已经取得了很大的成功。我们建议通过有效的一声机器人学习算法解决此问题，该算法围绕第三人称的角度学习。我们称我们的方法旋转：野生人类模仿机器人学习。旋转对人类演示者的意图提取先前，并使用它来初始化代理商的策略。我们介绍了一种有效的现实世界政策学习方案，该方案可以使用交互作用进行改进。我们的主要贡献是一种简单的基于抽样的策略优化方法，这是一种对齐人和机器人视频的新型目标功能，以及一种提高样本效率的探索方法。我们在现实世界中展示了单一的概括和成功，其中包括野外的20个不同的操纵任务。视频并在https://human2robot.github.io上进行交谈

第三人称视频的逆增强学习（IRL）研究表明，令人鼓舞的结果是消除了对机器人任务的手动奖励设计的需求。但是，大多数先前的作品仍然受到相对受限域视频领域的培训的限制。在本文中，我们认为第三人称IRL的真正潜力在于增加视频的多样性以更好地扩展。为了从不同的视频中学习奖励功能，我们建议在视频上执行图形抽象，然后在图表空间中进行时间匹配，以衡量任务进度。我们的见解是，可以通过形成图形的实体交互来描述任务，并且该图抽象可以帮助删除无关紧要的信息，例如纹理，从而产生更强大的奖励功能。我们评估了我们的方法，即Graphirl，关于X魔术中的跨体制学习，并从人类的示范中学习进行真实机器人操纵。我们对以前的方法表现出对各种视频演示的鲁棒性的显着改善，甚至比真正的机器人推动任务上的手动奖励设计获得了更好的结果。视频可从https://sateeshkumar21.github.io/graphirl获得。

我们研究了从机器人交互的大型离线数据集学习一系列基于视觉的操纵任务的问题。为了实现这一目标，人类需要简单有效地将任务指定给机器人。目标图像是一种流行的任务规范形式，因为它们已经在机器人的观察空间接地。然而，目标图像也有许多缺点：它们对人类提供的不方便，它们可以通过提供导致稀疏奖励信号的所需行为，或者在非目标达到任务的情况下指定任务信息。自然语言为任务规范提供了一种方便而灵活的替代方案，而是随着机器人观察空间的接地语言挑战。为了可扩展地学习此基础，我们建议利用具有人群源语言标签的离线机器人数据集（包括高度最佳，自主收集的数据）。使用此数据，我们学习一个简单的分类器，该分类器预测状态的更改是否完成了语言指令。这提供了一种语言调节奖励函数，然后可以用于离线多任务RL。在我们的实验中，我们发现，在语言条件的操作任务中，我们的方法优于目标 - 图像规格和语言条件仿制技术超过25％，并且能够从自然语言中执行Visuomotor任务，例如“打开右抽屉“和”移动订书机“，在弗兰卡·埃米卡熊猫机器人上。