Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

捍卫深层神经网络免受对抗性示例是AI安全的关键挑战。为了有效地提高鲁棒性，最近的方法集中在对抗训练中的决策边界附近的重要数据点上。但是，这些方法容易受到自动攻击的影响，这是无参数攻击的合奏，可用于可靠评估。在本文中，我们通过实验研究了其脆弱性的原因，发现现有方法会减少真实标签和其他标签的逻辑之间的利润，同时保持其梯度规范非微小值。减少的边缘和非微小梯度规范会导致其脆弱性，因为最大的logit可以轻松地被扰动翻转。我们的实验还表明，logit边缘的直方图具有两个峰，即小和大的logit边缘。从观察结果来看，我们提出了切换单重损失（SOVR），当数据具有较小的logit rumgins时，它会使用单重损失，从而增加边缘。我们发现，SOVR比现有方法增加了logit的利润率，同时使梯度规范保持较小，并且在针对自动攻击的鲁棒性方面超越了它们。

Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor.

Human pose estimation, particularly in athletes, can help improve their performance. However, this estimation is difficult using existing methods, such as human annotation, if the subjects wear loose-fitting clothes such as ski/snowboard wears. This study developed a method for obtaining the ground truth data on two-dimensional (2D) poses of a human wearing loose-fitting clothes. This method uses fast-flushing light-emitting diodes (LEDs). The subjects were required to wear loose-fitting clothes and place the LED on the target joints. The LEDs were observed directly using a camera by selecting thin filmy loose-fitting clothes. The proposed method captures the scene at 240 fps by using a high-frame-rate camera and renders two 30 fps image sequences by extracting LED-on and -off frames. The temporal differences between the two video sequences can be ignored, considering the speed of human motion. The LED-on video was used to manually annotate the joints and thus obtain the ground truth data. Additionally, the LED-off video, equivalent to a standard video at 30 fps, confirmed the accuracy of existing machine learning-based methods and manual annotations. Experiments demonstrated that the proposed method can obtain ground truth data for standard RGB videos. Further, it was revealed that neither manual annotation nor the state-of-the-art pose estimator obtains the correct position of target joints.

Robotic hands with soft surfaces can perform stable grasping, but the high friction of the soft surfaces makes it difficult to release objects, or to perform operations that require sliding. To solve this issue, we previously developed a contact area variable surface (CAVS), whose friction changed according to the load. However, only our fundamental results were previously presented, with detailed analyses not provided. In this study, we first investigated the CAVS friction anisotropy, and demonstrated that the longitudinal direction exhibited a larger ratio of friction change. Next, we proposed a sensible CAVS, capable of providing a variable-friction mechanism, and tested its sensing and control systems in operations requiring switching between sliding and stable-grasping modes. Friction sensing was performed using an embedded camera, and we developed a gripper using the sensible CAVS, considering the CAVS friction anisotropy. In CAVS, the low-friction mode corresponds to a small grasping force, while the high-friction mode corresponds to a greater grasping force. Therefore, by controlling only the friction mode, the gripper mode can be set to either the sliding or stable-grasping mode. Based on this feature, a methodology for controlling the contact mode was constructed. We demonstrated a manipulation involving sliding and stable grasping, and thus verified the efficacy of the developed sensible CAVS.

提出了一种表示每个数据集的消化信息的方法，以创新思想的帮助以及试图使用或组合数据集创建有价值的产品，服务和业务模型的数据用户的通信。与通过共享属性（即变量）连接数据集的方法相比，此方法通过在现实世界中应活跃的情况下通过事件，情况或操作连接数据集。该方法反映了每个元数据对特征概念的适应性的考虑，这是预期从数据中获得的信息或知识的摘要；因此，数据的用户获得了适合真实企业和现实生活需求的实践知识，以及将AI技术应用于数据的基础。

我们将2D盲点估计作为道路场景理解的关键视觉任务。通过自动检测从车辆有利位置阻塞的道路区域，我们可以主动提醒手动驾驶员或自动驾驶系统，以实现事故的潜在原因（例如，引起人们对孩子可能逃脱的道路区域的注意）。在完整3D中检测盲点将是具有挑战性的，因为即使汽车配备了LIDAR，3D推理也会非常昂贵且容易发生。相反，我们建议从单眼相机中学习估计2D中的盲点。我们通过两个步骤实现这一目标。我们首先引入了一种自动方法，用于通过利用单眼深度估计，语义细分和SLAM来生成``地面真相''盲点训练数据，以进行任意驾驶视频。关键的想法是在3D中推理，但要从2D图像定义为那些目前看不见但在不久的将来看到的道路区域。我们使用此自动离线盲点估计来构建一个大规模数据集，我们称之为道路盲点（RBS）数据集。接下来，我们介绍BlindSpotnet（BSN），这是一个简单的网络，该网络完全利用此数据集，以完全自动估算框架盲点概率图，以用于任意驾驶视频。广泛的实验结果证明了我们的RBS数据集的有效性和BSN的有效性。

我们引入了责任感敏感安全性（RSS）的目标延长，这是一种基于规则的自动驾驶系统安全保证（ADS）的方法。制定RSS规则保证目标实现 - 除了原始RSS中的避免碰撞外，还需要进行长时间的操纵序列的复杂计划。为了应对复杂性，我们基于程序逻辑引入了一个构图推理框架，其中可以系统地为较小的子赛车制定RSS规则，并将它们组合起来以获取用于较大场景的RSS规则。作为框架的基础，我们介绍了一个程序逻辑DFHL，可满足连续的动态和安全条件。我们的框架介绍了基于DFHL的工作流程，用于导出目标感知RSS规则；我们也讨论其软件支持。我们在安全体系结构中使用RSS规则进行了实验评估。它的结果表明，目标感知RSS确实有效地实现了避免碰撞和目标实现目标。

室内环境中的热舒适感会对乘员的健康，福祉和表现产生巨大影响。鉴于对能源效率和实现智能建筑的关注，机器学习（ML）越来越多地用于数据驱动的热舒适度（TC）预测。通常，提出了用于空调或HVAC通风建筑物的基于ML的解决方案，这些模型主要是为成年人设计的。另一方面，在大多数国家 /地区，自然通风（NV）的建筑物是常态。它们也是节能和长期可持续性目标的理想选择。但是，NV建筑物的室内环境缺乏热调节，并且在空间环境中差异很大。这些因素使TC预测极具挑战性。因此，确定建筑环境对TC模型性能的影响很重要。此外，需要研究跨不同NV室内空间的TC预测模型的概括能力。这项工作解决了这些问题。数据是通过在5个自然通风的学校建筑中进行的为期一个月的实地实验，涉及512名小学生。空间变异性对学生舒适度的影响通过预测准确性的变化（高达71％）来证明。还通过特征重要性的变化来证明建筑环境对TC预测的影响。此外，对儿童（我们的数据集）和成人（ASHRAE-II数据库）进行了模型性能的空间变异性比较分析。最后，评估了NV教室中热舒适模型的概括能力，并强调了主要挑战。

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。