This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

在本文中，我们考虑了在具有多个自动机器人的系统中分配人类操作员协助的问题。每个机器人都需要完成独立任务，每个任务定义为一系列任务。在执行任务时，机器人可以自主操作，也可以由人类操作员远程执行，以更快地完成任务。我们表明，创建详细时间表的问题使系统的制造量最小化是NP-HARD。我们将问题提出为混合整数线性程序，可用于最佳地解决小到中等大小的问题实例。我们还开发了一种随时随地的算法，该算法利用问题结构来提供对操作员调度问题的快速和高质量解决方案，即使对于更大的问题实例也是如此。我们的关键见解是在贪婪创建的时间表中识别阻止任务，并迭代地删除这些块以提高解决方案的质量。通过数值模拟，我们证明了所提出的算法的好处是一种高于其他贪婪方法的有效且可扩展的方法。

在本文中，我们研究了众所周知的团队导演问题，其中一批机器人通过访问地点收集奖励。通常，假设奖励是机器人已知的;但是，在环境监测或场景重建的应用中，奖励通常是主观的，并指定它们是具有挑战性的。我们提出了一个框架来通过向它们呈现替代解决方案来学习用户的未知偏好，并且用户在所提出的替代解决方案上提供排名。我们考虑了用户的两种情况：1）确定替代解决方案的最佳排名的确定性用户，以及根据未知概率分布提供最佳排名的噪声用户。对于确定性用户，我们提出了一个框架，以最大限度地减少与最佳解决方案的最大偏差的界限，即后悔。我们适应捕获嘈杂用户的方法，并最大限度地减少预期的遗憾。最后，我们展示了学习用户偏好的重要性以及在广泛的实验结果中使用真实的世界数据集进行环境监测问题的大量实验结果的性能。

在本文中，我们考虑在具有多个半自治机器人的系统中分配人类运营商的问题。每个机器人都需要执行独立的任务序列，经历了一次失败并在每个任务时陷入故障状态的可能性。如果需要，人类运营商可以帮助或漫游机器人。传统的MDP技术用于解决这些问题的面临可扩展性问题，因为具有机器人和运营商的数量的状态和行动空间的指数增长。在本文中，我们推出了操作员分配问题可转向的条件，从而实现了削弱指数启发式的使用。可以容易地检查条件以验证可索引性，我们表明他们持有广泛的兴趣问题。我们的主要洞察力是利用各个机器人的价值函数的结构，从而导致可以针对每个机器人的每个状态分开验证的条件。我们将这些条件应用于远程机器人监控系统中常见的两种转换。通过数值模拟，我们展示了削减指数政策作为近乎最佳和可扩展方法的功效，以实现现有的可扩展方法。

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

我们提出了Blenderbot 3，这是一个175B参数对话模型，能够通过访问Internet和长期内存进行开放域对话，并接受了大量用户定义的任务的培训。我们同时发布了模型权重和代码，还将模型部署在公共网页上，以与有机用户进行交互。该技术报告描述了该模型的构建方式（建筑，模型和培训计划）以及其部署的细节，包括安全机制。人类评估表明，它优于现有的开放域对话代理，包括其前身（Roller等，2021； Komeili等，2022）。最后，我们使用部署收集的数据详细介绍了持续学习的计划，该数据也将公开发布。因此，该研究计划的目标是使社区能够研究通过互动学习的不断改进的负责任的代理商。

单像素成像（SPI）是一种新型成像技术，其工作原理基于压缩感（CS）理论。在SPI中，数据是通过一系列压缩测量获得的，并重建了相应的图像。通常，重建算法（例如基础追求）依赖于图像中的稀疏性假设。但是，深度学习的最新进展发现了其在重建CS图像中的用途。尽管在模拟中显示出令人鼓舞的结果，但通常不清楚如何在实际的SPI设置中实现这种算法。在本文中，我们证明了对SPI图像的重建以及块压缩感（BCS）的重建。我们还提出了一个基于卷积神经网络的新型重建模型，该模型优于其他竞争性CS重建算法。此外，通过将BCS合并到我们的深度学习模型中，我们能够重建以上图像大小以上的任何大小的图像。此外，我们表明我们的模型能够重建从SPI设置获得的图像，同时接受自然图像进行训练，这可能与SPI图像大不相同。这为CS重建来自各个领域的图像重建的深度学习模型的可行性打开了机会。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。