尽管大多数微型机器人在坚固耐用的地形上都面临困难，但甲虫可以在复杂的底物上平稳行走而不会滑倒或粘在地面上，因为它们的刚度可变可变的塔西（Tarsi）和可在塔西（Tarsi）的尖端上伸展的钩子。在这项研究中，我们发现甲虫会积极弯曲并定期扩大爪子以在网状表面上自由爬行。受甲虫的爬行机制的启发，我们设计了一个8厘米的微型攀岩机器人，以与天然甲虫相同的循环方式打开和弯曲的人造爪。机器人可以在网格表面上以可控步态自由攀爬，陡峭的斜角60 {\ deg}，甚至过渡表面。据我们所知，这是第一个可以同时攀登网格表面和悬崖倾斜的微型机器人。

构建复杂三维（3D）塑料部件上的精确微纳米金属图案允许制造用于先进应用的功能装置。但是，这种图案目前是昂贵的，需要具有长制造时间的复杂过程。本作者演示了一种用任意复杂的形状制造微纳米3D金属塑料复合结构的方法。在这种方法中，修饰光固化树脂以制备能够允许随后的化学镀（ELP）的活性前体。新开发了一种多材料数字光处理3D打印机，以使含有由标准树脂或彼此嵌套的标准树脂或有源前体树脂制成的区域的部件的制造。这些部件的选择性3D ELP处理提供了各种金属塑料复合部件，其具有复杂的中空微纳米结构，其尺寸小于40μm的尺寸规模特定的拓扑关系。使用这种技术，可以通过传统方法制造的3D金属拓扑，并且可以在塑料部件内产生金属图案作为进一步小型化电子设备的装置。所提出的方法还可以产生具有改善金属粘附到塑料基材的金属涂层。基于该技术，设计并制造了由不同功能性非金属材料和特定金属图案组成的几种传感器。本结果证明了该方法的可行性，并提出了智能3D微纳米电子，3D可穿戴设备，微/纳米传感器和医疗保健领域的潜在应用。

虽然工程师在建造昆虫的昆虫规模微空中飞行器（Mavs）时，工程师进行了许多努力，资源和时间，但昆虫本身就是真正的飞行大师。如果我们将生活昆虫作为MAV的平台，那么怎么办？在这里，我们通过昆虫计算机混合机器人的飞行肌肉的电气刺激报告了飞行控制，这是可安装无线背包控制器和活甲虫的界面。甲虫使用间接飞行肌肉来驱动翼缝和三个主要的直接飞行肌肉（Basalar，Sumalar和第三腋（3AX）肌肉）来控制飞行机动的翅膀的运动学。虽然通过刺激基石和3辆肌肉已经实现了转向控制，但亚脚肌的电刺激导致飞行中的制动和高度控制。我们还通过刺激单个亚脚轮肌肉展示大约20度的对侧偏航和滚动。刺激亚级肌肉的肌肉增加20度，并将飞行减速1.5 m / s2，以及诱导2 m / s2的升高。

Target Propagation (TP) is a biologically more plausible algorithm than the error backpropagation (BP) to train deep networks, and improving practicality of TP is an open issue. TP methods require the feedforward and feedback networks to form layer-wise autoencoders for propagating the target values generated at the output layer. However, this causes certain drawbacks; e.g., careful hyperparameter tuning is required to synchronize the feedforward and feedback training, and frequent updates of the feedback path are usually required than that of the feedforward path. Learning of the feedforward and feedback networks is sufficient to make TP methods capable of training, but is having these layer-wise autoencoders a necessary condition for TP to work? We answer this question by presenting Fixed-Weight Difference Target Propagation (FW-DTP) that keeps the feedback weights constant during training. We confirmed that this simple method, which naturally resolves the abovementioned problems of TP, can still deliver informative target values to hidden layers for a given task; indeed, FW-DTP consistently achieves higher test performance than a baseline, the Difference Target Propagation (DTP), on four classification datasets. We also present a novel propagation architecture that explains the exact form of the feedback function of DTP to analyze FW-DTP.

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.

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.

Telework "avatar work," in which people with disabilities can engage in physical work such as customer service, is being implemented in society. In order to enable avatar work in a variety of occupations, we propose a mobile sales system using a mobile frozen drink machine and an avatar robot "OriHime", focusing on mobile customer service like peddling. The effect of the peddling by the system on the customers are examined based on the results of video annotation.

In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.

使用三维（3D）图像传感器的智能监视一直在智能城市的背景下引起人们的注意。在智能监控中，实施了3D图像传感器获取的点云数据的对象检测，以检测移动物体（例如车辆和行人）以确保道路上的安全性。但是，由于光检测和范围（LIDAR）单元用作3D图像传感器或3D图像传感器的安装位置，因此点云数据的特征是多元化的。尽管迄今已研究了从点云数据进行对象检测的各种深度学习（DL）模型，但尚无研究考虑如何根据点云数据的功能使用多个DL模型。在这项工作中，我们提出了一个基于功能的模型选择框架，该框架通过使用多种DL方法并利用两种人工技术生成的伪不完整的训练数据来创建各种DL模型：采样和噪声添加。它根据在真实环境中获取的点云数据的功能，为对象检测任务选择最合适的DL模型。为了证明提出的框架的有效性，我们使用从KITTI数据集创建的基准数据集比较了多个DL模型的性能，并比较了通过真实室外实验获得的对象检测的示例结果。根据情况，DL模型之间的检测准确性高达32％，这证实了根据情况选择适当的DL模型的重要性。

本文从未分割的烹饪视频中解决了食谱生成，该任务要求代理（1）提取完成盘子时提取关键事件，以及（2）为提取的事件生成句子。我们的任务类似于密集的视频字幕（DVC），该字幕旨在彻底检测事件并为其生成句子。但是，与DVC不同，在食谱生成中，食谱故事意识至关重要，模型应以正确的顺序输出适当数量的关键事件。我们分析了DVC模型的输出，并观察到，尽管（1）几个事件可作为食谱故事采用，但（2）此类事件的生成句子并未基于视觉内容。基于此，我们假设我们可以通过从DVC模型的输出事件中选择Oracle事件并为其重新生成句子来获得正确的配方。为了实现这一目标，我们提出了一种基于变压器的新型训练事件选择器和句子生成器的联合方法，用于从DVC模型的输出中选择Oracle事件并分别为事件生成接地句子。此外，我们通过包括成分来生成更准确的配方来扩展模型。实验结果表明，所提出的方法优于最先进的DVC模型。我们还确认，通过以故事感知方式对食谱进行建模，提出的模型以正确的顺序输出适当数量的事件。