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.

We propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

In recent years, the performance of novel view synthesis using perspective images has dramatically improved with the advent of neural radiance fields (NeRF). This study proposes two novel techniques that effectively build NeRF for 360{\textdegree} omnidirectional images. Due to the characteristics of a 360{\textdegree} image of ERP format that has spatial distortion in their high latitude regions and a 360{\textdegree} wide viewing angle, NeRF's general ray sampling strategy is ineffective. Hence, the view synthesis accuracy of NeRF is limited and learning is not efficient. We propose two non-uniform ray sampling schemes for NeRF to suit 360{\textdegree} images - distortion-aware ray sampling and content-aware ray sampling. We created an evaluation dataset Synth360 using Replica and SceneCity models of indoor and outdoor scenes, respectively. In experiments, we show that our proposal successfully builds 360{\textdegree} image NeRF in terms of both accuracy and efficiency. The proposal is widely applicable to advanced variants of NeRF. DietNeRF, AugNeRF, and NeRF++ combined with the proposed techniques further improve the performance. Moreover, we show that our proposed method enhances the quality of real-world scenes in 360{\textdegree} images. Synth360: https://drive.google.com/drive/folders/1suL9B7DO2no21ggiIHkH3JF3OecasQLb.

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.

Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl

本文档描述了Spotify出于学术研究目的发布的葡萄牙语播客数据集。我们概述了如何采样数据，有关集合的一些基本统计数据，以及有关巴西和葡萄牙方言的分发信息的简要信息。

临床文本的自动汇总可以减轻医疗专业人员的负担。 “放电摘要”是摘要的一种有希望的应用，因为它们可以从每日住院记录中产生。我们的初步实验表明，放电摘要中有20-31％的描述与住院记录的内容重叠。但是，目前尚不清楚如何从非结构化来源生成摘要。为了分解医师的摘要过程，本研究旨在确定摘要中的最佳粒度。我们首先定义了具有不同粒度的三种摘要单元，以比较放电摘要生成的性能：整个句子，临床段和条款。我们在这项研究中定义了临床细分，旨在表达最小的医学意义概念。为了获得临床细分，有必要在管道的第一阶段自动拆分文本。因此，我们比较了基于规则的方法和一种机器学习方法，而后者在分裂任务中以0.846的F1得分优于构造者。接下来，我们在日本的多机构国家健康记录上，使用三种类型的单元（基于Rouge-1指标）测量了提取性摘要的准确性。使用整个句子，临床段和条款分别为31.91、36.15和25.18的提取性摘要的测量精度分别为31.91、36.15和25.18。我们发现，临床细分的准确性比句子和条款更高。该结果表明，住院记录的汇总需要比面向句子的处理更精细的粒度。尽管我们仅使用日本健康记录，但可以解释如下：医生从患者记录中提取“具有医学意义的概念”并重新组合它们...

深度神经网络（DNN）众所周知，很容易受到对抗例子的影响（AES）。此外，AE具有对抗性可传递性，这意味着为源模型生成的AE可以以非平凡的概率欺骗另一个黑框模型（目标模型）。在本文中，我们首次研究了包括Convmixer在内的模型之间的对抗性转移性的属性。为了客观地验证可转让性的属性，使用称为AutoAttack的基准攻击方法评估模型的鲁棒性。在图像分类实验中，Convmixer被确认对对抗性转移性较弱。