We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by numerical simulations.

The demand for resilient logistics networks has increased because of recent disasters. When we consider optimization problems, entropy regularization is a powerful tool for the diversification of a solution. In this study, we proposed a method for designing a resilient logistics network based on entropy regularization. Moreover, we proposed a method for analytical resilience criteria to reduce the ambiguity of resilience. First, we modeled the logistics network, including factories, distribution bases, and sales outlets in an efficient framework using entropy regularization. Next, we formulated a resilience criterion based on probabilistic cost and Kullback--Leibler divergence. Finally, our method was performed using a simple logistics network, and the resilience of the three logistics plans designed by entropy regularization was demonstrated.

In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance and communication savings in scenarios wherein the number of agents differs from that in the training environment. We confirm the effectiveness of our approach by using a maximum of eight and six robots in the simulations and experiments, respectively.

多个空中机器人的合作运输有可能支持各种有效载荷，并减少他们被丢弃的可能性。此外，自动控制的机器人使系统相对于有效载荷可扩展。在本研究中，使用刚性附加的空中机器人开发了合作运输系统，并提出了一种分散的控制器，以保证未知严格正实际系统的跟踪误差的渐近稳定性。反馈控制器用于使用共享附件位置将不稳定的系统转换为严格的正实真实的系统。首先，通过数值模拟研究了具有比载体机器人大的不同形状的未知有效载荷的合作运输。其次，使用八个机器人在机器人失败下，使用八个机器人进行了未知有效载荷（重量约为2.7千克，最大长度为1.6米的重量）的合作运输。最后，表明所提出的系统携带了一个未知的有效载荷，即使附着位置未被共享，即，即使不严格保证渐近稳定性也是如此。

We construct a corpus of Japanese a cappella vocal ensembles (jaCappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka. The variety in genre and voice part match vocal ensembles recently widespread in social media services such as YouTube, although the main targets of conventional vocal ensemble datasets are choral singing made up of soprano, alto, tenor, and bass. Experimental evaluation demonstrates that our corpus is a challenging resource for vocal ensemble separation. Our corpus is available on our project page (https://tomohikonakamura.github.io/jaCappella_corpus/).

联合学习（FL）允许许多代理参与培训全球机器学习模型，而无需透露本地存储的数据。与传统的分布式学习相比，药物的异质性（非IID）减慢了FL中的收敛性。此外，许多数据集太嘈杂或太小，很容易被复杂模型（例如深神经网络）过度拟合。在这里，我们考虑在嘈杂，分层和表格数据集上使用FL回归的问题，在该数据集中，用户分布有显着差异。受潜在类回归（LCR）的启发，我们提出了一种新颖的概率模型，分层潜在阶级回归（HLCR）及其扩展到联邦学习的扩展。 FEDHLCR由线性回归模型的混合物组成，比简单的线性回归允许更好的准确性，同时保持其分析性能并避免过度拟合。我们的推论算法源自贝叶斯理论，为过度拟合提供了强大的融合保证和良好的鲁棒性。实验结果表明，FedHLCR即使在非IID数据集中也提供快速收敛。

HottoPixx，由Bittorf等人提出。在NIPS 2012，是一种解决可分离假设下的非负矩阵分子（NMF）问题的算法。可分离的NMFS具有重要的应用程序，例如从文档和超光图像的文件提取主题。在这种应用中，算法对噪声的稳健性是成功的关键。HottoPixx已被证明对噪声具有稳健性，并且可以通过后处理进一步增强其鲁棒性。但是，有一个缺点。HottoPixx及其后处理要求我们估计我们想要在运行之前进行分解的矩阵中涉及的噪声水平，因为它们将其用作输入数据的一部分。噪声级别估计不是一项简单的任务。在本文中，我们克服了这个缺点。我们在没有先前了解噪声水平的情况下，我们介绍了HottoPixx的改进及其后处理。我们表明细化与原始算法具有几乎与噪声相同的稳健性。