Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting large-scale robotic data is much more expensive and slower as each operator can control only a single robot at a time. To make this costly data collection process efficient and scalable, we propose Policy Assisted TeleOperation (PATO), a system which automates part of the demonstration collection process using a learned assistive policy. PATO autonomously executes repetitive behaviors in data collection and asks for human input only when it is uncertain about which subtask or behavior to execute. We conduct teleoperation user studies both with a real robot and a simulated robot fleet and demonstrate that our assisted teleoperation system reduces human operators' mental load while improving data collection efficiency. Further, it enables a single operator to control multiple robots in parallel, which is a first step towards scalable robotic data collection. For code and video results, see https://clvrai.com/pato

以人为本的人工智能考虑了人工智能表现的经验。尽管丰富的研究一直在通过全自动或弱监督学习来帮助AI实现超人类的表现，但较少的努力正在尝试AI如何量身定制人类对人类首选技能水平的限制。在这项工作中，我们指导课程加强学习结果朝着首选的绩效水平，通过从人类的决策过程中学习而不是太困难也不容易。为了实现这一目标，我们开发了一个便携式交互式平台，使用户能够通过操纵任务难度，观察性能并提供课程反馈来在线与代理商进行交互。我们的系统高度可行，使人类可以训练大规模的增强学习应用程序，这些学习应用需要数百万没有服务器的样品。结果证明了互动课程对涉及人类在环的增强学习的有效性。它显示强化学习绩效可以成功地与人类所需的难度水平同步调整。我们认为，这项研究将为实现流动和个性化的适应性困难打开新的大门。

我们提出了一种从人类设计的家具布局数据中生成室内家具的布置的方法。我们的方法创建了针对指定多样性的安排，例如房间中所有家具的总价格以及放置的碎片数量。为了产生逼真的家具布置，我们在人类设计的布局上训练生成的对抗网络（GAN）。为了针对安排中的特定多样性，我们通过质量多样性算法优化GAN的潜在空间，以生成多样化的安排集合。实验表明，我们的方法发现了一系列与人类设计的布局相似的布置，但价格和家具的数量也有所不同。

加强学习的最新进展（RL）已开始生产能够解决复杂环境分布的通常能力的代理。这些试剂通常在固定的，人为实现的环境上进行测试。另一方面，质量多样性（QD）优化已被证明是环境生成算法的有效组成部分，该算法可以产生多种多样的最终代理行为的高质量环境集合。但是，这些算法需要在新生成的环境上对代理的潜在昂贵模拟。我们提出了深层替代辅助生成环境（DSAGE），这是一种样本效率的QD环境生成算法，该算法保持了一个深层的替代模型，用于预测新环境中的试剂行为。结果有两个基准域，表明DSAGE明显优于现有的QD环境生成算法，这些算法在发现了引起最先进的RL代理商和计划代理的各种行为的环境集合中。

In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without this knowledge, a robot may infer incorrect reward functions that lead to undesirable or unsafe control policies. Recent work has proposed an LfD framework where a user provides a set of formal task specifications to guide LfD, to address the challenge of reward shaping. However, in this framework, specifications are manually ordered in a performance graph (a partial order that specifies relative importance between the specifications). The main contribution of this paper is an algorithm to learn the performance graph directly from the user-provided demonstrations, and show that the reward functions generated using the learned performance graph generate similar policies to those from manually specified performance graphs. We perform a user study that shows that priorities specified by users on behaviors in a simulated highway driving domain match the automatically inferred performance graph. This establishes that we can accurately evaluate user demonstrations with respect to task specifications without expert criteria.

我们研究了在游戏中有效地产生高质量和多样化的内容的问题。以前的HESTETHSTONE上自动化牌照的工作表明，质量多样性算法MAP-ELITE可以生成具有不同战略游戏的高性能甲板的集合。但是，Map-Elites需要大量昂贵的评估来发现甲板的各种集合。我们建议使用在线培训的深度代理模型进行地图精英，以预测关于候选甲板的游戏结果。 Map-Elites发现了一个不同的数据集，以提高代理模型精度，而代理模型有助于指导地图精英迈向有希望的新内容。在炉石甲板德克布布布尔案例研究中，我们表明我们的方法提高了Map-Elites的样本效率，并且优于随机甲板训练的模型，以及线性代理模型基线，设置了新的最先进的自动炉石德克斯普通应用领域的质量多样性方法。

为了根据他们在装配任务中的个人偏好，机器人在给定任务中需要用户演示。但是，在实际装配任务中提供示范可能是乏味且耗时的。我们的论文是，我们可以从代表性规范任务中的演示中学习装配任务中的用户偏好。受到以前的人类运动经济的启发，我们建议将用户偏好作为抽象任务 - 不可行特征的线性函数，例如用户所需的运动和身体和心理工作。对于每个用户，我们从规范任务中的演示中学习他们的偏好，并使用学习的偏好来预测他们在实际装配任务中的行为，而实际任务中的任何用户演示。我们在模型 - 飞机组装研究中评估我们提出的方法，并表明偏好可以有效地从规范转移到实际装配任务，使机器人能够预测用户动作。

人们经常在网上观看视频，以学习如何烹饪新食谱，组装家具或维修计算机。我们希望启用具有相同功能的机器人。这是具有挑战性的；操作动作的差异很大，有些视频甚至涉及多个人，他们通过共享和交换对象和工具来协作。此外，学习的表示形式需要足够通用才能转移到机器人系统中。另一方面，以前的工作表明，人类操纵动作的空间具有语言，层次结构，将动作与操纵对象和工具联系起来。在这种行动的语言理论的基础上，我们提出了一个系统，以理解和执行从网络上的全长真实烹饪视频中展示的动作序列。该系统将输入作为一个新的，以前看不见的烹饪视频，并用对象标签和边界框注释，并为一个或多个机器人臂输出协作操作操作计划。我们在100个YouTube烹饪视频的标准化数据集以及六个完整的YouTube视频中演示了该系统的性能，其中包括两个参与者之间的协作动作。我们将系统与由最先进的动作检测基线组成的基线系统进行比较，并显示我们的系统达到了更高的动作检测准确性。我们还提出了一个开源平台，用于在模拟环境以及实际机器人部门中执行学习的计划。

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference.