論文 - 田向　権

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り

Ono Tomohiro, Suzuki Akihiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics )   28   420 - 424   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Manual annotation is common, but problems occur, such as oversight and mislabeling via human error. These problems are known to affect the quality of datasets significantly. To resolve these problems, we propose a method to automatically generate high-quality and large datasets in a short time using a simulator. Our proposed method uses domain randomization to minimize domain gaps without faithfully reproducing real scenes. The generated dataset achieved more than 80% recognition accuracy against the real image dataset.

DOI： 10.5954/icarob.2023.os17-6

CiNii Research

その他リンク： https://kyutech.repo.nii.ac.jp/records/7911

Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り

Yano Yuga, Fukuda Yukiya, Ono Tomohiro, Tamukoh Hakaru

人工生命とロボットに関する国際会議予稿集 ( 株式会社ALife Robotics )   28   409 - 414   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

We propose an efficient semantic map to realize flexible human-robot interaction (HRI) in domestic environments. Our proposed map is created from an output of Simultaneous Localization and Mapping and already-known environmental information such as furniture and room. In this study, we evaluated the effectiveness of our proposed method on two benchmark tests for HRI in RoboCup@Home held in Bangkok in 2022. In the RoboCup@Home, we employ 3D human recognition to apply our proposed map to HRI, such as "find and offer an empty seat." Our proposed method had the best score of all teams on both tests. The results of our experiments are available at https://youtube.com/playlist?list=PLfbN50Mwh2DG3OPDeCHo4TNuyrU4qYCrJ.

DOI： 10.5954/icarob.2023.os17-4

CiNii Research

その他リンク： https://kyutech.repo.nii.ac.jp/records/7918

Multiple memory accumulation and recall by a hippocampus model reflecting emotional values 査読有り 国際誌

Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie

The 11th RIEC International Symposium on Brain Functions and Brain Computer   2023年02月

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

未知視点に対する疑似特徴ベクトル評価によるFew-shot NeRFの検討 国際誌

金岡 大樹，薗頭 元春，田向 権，川西 康友

研究報告コンピュータビジョンとイメージメディア（CVIM )電子情報通信学会パターン認識・メディア理解研究会（PRMU） ( 電子情報通信学会パターン認識・メディア理解研究会（PRMU） )   55 ( 1 )   6   2023年02月

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記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）

任意視点画像生成において Neural Radiance Fields は非常に強力な手法ではあるが，学習のためには多くの画像が必要である．本発表では，パラメトリック固有空間法のアナロジーとして，教師データが存在しない未知視点において，近傍視点の画像から擬似的に生成した特徴ベクトルを教師信号として学習に用いることで，少数の画像でも学習できる Manifold NeRF を提案する．実験の結果，疑似教師信号を用いることで，データセットが少量でも精度良く任意視点画像が生成できることを確認した．

Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り 国際誌

Yuma Yoshimoto and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

Object Search and Empty Space Detection System for Home Service Robot 査読有り 国際誌

Tomoya Shiba, Tomohiro Ono and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り 国際誌

Ikuya Matsumoto, Daiju Kanaoka and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

Flexible Human-Robot Interaction in Domestic Environment Using Semantic Map 査読有り 国際誌

Yuga Yano, Yukiya Fukuda, Tomohiro Ono, and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

Impact of PS load on FPGA object detection system performance 査読有り 国際誌

Yusuke Watanabe and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization 査読有り 国際誌

Tomohiro Ono, Akihiro Suzuki and Hakaru Tamukoh

The 2023 International Conference on Artificial Life and Robotics (ICAROB2023)   2023年02月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

Traffic Flow Optimization using a Chaotic Boltzmann Machine Annealer on an FPGA 査読有り

Yoshioka K., Tanaka Y., Tamukoh H.

Proceedings - International Conference on Field-Programmable Technology, ICFPT   266 - 267   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

We demonstrate a chaotic Boltzmann machine annealer, which is a field-programmable gate array (FPGA)-based high-performance annealing machine, and a control system powered by two open-source software packages. The annealer is employed in solving a large-scale real-world optimization problem, the traffic flow optimization. This involves distributing 500 car routes between the Haneda Airport area to the Pacifico Yokohama area, the venue of the International Conference on Field Programmable Technology 2023, while minimizing the total driving distance and preventing traffic jams. We obtain solutions that are comparable in accuracy to solutions of simulated annealing running on a graphics processing unit (GPU-SA) and a central processing unit (CPU-SA). The annealing machine on an FPGA is approximately 571 and 97600 times as fast as the GPUSA and the CPU-SA, respectively. We will demonstrate solving traffic flow optimization in the towns familiar to the visitors.

DOI： 10.1109/ICFPT59805.2023.00038

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187557051&origin=inward

In-material reservoir implementation of reservoir-based convolution 査読有り

Tanaka Y., Usami Y., Tanaka H., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems   2023-May   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This study aims to implement a reservoir-based convolutional neural network (CNN) on physical reservoir computing (RC) to develop an efficient image recognition system for edge AI. Therefore, we propose a novel reservoir-based convolution circuit system that uses in-material reservoir computing, a type of physical RC made from a sulfonated polyaniline network. The experimental results demonstrate that the proposed circuit system extracts image features in the same way as the original CNN and that a reservoir-based CNN on the in-material RC achieves an accuracy rate of 81.7% in an image classification task while an echo state network-based CNN achieves 87.7%.

DOI： 10.1109/ISCAS46773.2023.10181436

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85167720431&origin=inward

In-Material Reservoir Implementation of reservoir-Based Convolution 査読有り 国際誌

Yuichiro Tanaka, Yuki Usami, Hirofumi Tanaka Hakaru Tamukoh

IEEE International Symposium on Circuits and Systems (ISCAS2023)   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

California, United States of America  

ホームサービスロボットのための高精度な特定人物追跡システムの開発 査読有り

小野 智寛, 阿部 佑志, 石田 裕太郎, 柴田 智広, 田向 権

日本ロボット学会誌 ( 一般社団法人 日本ロボット学会 )   41 ( 2 )   198 - 201   2023年01月

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担当区分：最終著者   記述言語：日本語   掲載種別：研究論文（学術雑誌）

<p>In this paper, we propose a highly accurate specific person tracking system for home service robots. This study focuses on Selected Online Ada-Boosting (SOAB) and Kernelized Correlation Filters (KCF). It proposes the Selected KCF (SKCF), which combines features of fast adaptation to a specific person and robustness for occlusion. We also propose a system that combines You Only Look Once (YOLO), a real-time object detection algorithm, with SKCF to initialize and recover the tracking system. The proposed system runs at 17 [fps] in an experimental environment with an Intel Core i7-8700K CPU and an NVIDIA GTX 1080 GPU. We also show that the tracking accuracy of the proposed system on public datasets for tracking specific persons is better than that of conventional methods. </p>

DOI： 10.7210/jrsj.41.198

Kyutacar

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/jrsj/41/2/41_41_198/_pdf

Robust Classification Model with Multimodal Learning for Home Service Robots 査読有り

Matsumoto I., Kanaoka D., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics   407 - 412   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

We propose an auxiliary data stream structure as a robust classification model. The model treats one modal as the main input and the other modals as supports. The model chooses how much of the sub-modal is used for classification. We experimented with two and three modal inputs. Moreover, we added pseudo-shadows to the visual information for the experiment with three modal inputs. In all experiments, our proposed model improves the accuracy and robustness to environmental disturbances by using multiple modals.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149480798&origin=inward

Self-Organizing Multiple Readouts for Reservoir Computing 査読有り

Tanaka Y., Tamukoh H.

IEEE Access   11   138839 - 138849   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

With advancements in deep learning (DL), artificial intelligence (AI) technology has become an indispensable tool. However, the application of DL incurs significant computational costs, making it less viable for edge AI scenarios. Consequently, the demand for cost-effective AI solutions, other than DL-based approaches, is increasing. Reservoir computing (RC) has attracted interest owing to its ability to provide low-cost training alternatives, holding great promise for edge AI applications. However, the training capability of RC is constrained by its reliance on a single linear layer, while weight connections in the remaining layers remain static during training. Moreover, accomplishing continuous learning tasks is difficult owing to the catastrophic forgetting in the linear layer. Therefore, we propose the integration of self-organizing multiple readouts to enhance RC's training capability. Our method distributes training data across multiple readouts, which prevents catastrophic forgetting of readouts and empowers each readout to adeptly assimilate new data, thereby elevating the overall training performance. The self-organizing function, which assigns similar data to the same readout, optimizes the memory utilization of these multiple readouts. Experimental results show that an RC equipped with the proposed multiple readouts successfully solved a continuous learning task by mitigating catastrophic forgetting because of the data distribution to the multiple readouts. Additionally, the RC achieved higher accuracy in a sound recognition task compared with the existing RC paradigm because of ensemble learning in the multiple readouts. Multiple readouts are effective in enhancing the training capability of RC and can contribute to the realization of RC applications.

DOI： 10.1109/ACCESS.2023.3340311

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179802812&origin=inward

Reservoir-based 1D convolution: low-training-cost AI 査読有り 国際誌

TANAKA Yuichiro, TAMUKOH Hakaru

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ( 一般社団法人 電子情報通信学会 )   advpub ( 0 )   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

<p>In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.</p>

DOI： 10.1587/transfun.2023eal2050

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2023EAL2050/_pdf

Object Search and Empty Space Detection System for Home Service Robot 査読有り

Shiba T., Tomohiro O., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics   402 - 406   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Home service robots have pick-and-place tasks to grasp and carry objects. When performing pick-and-place tasks, robots need to process to search for objects in the case of missing a target object. We propose approaches for the robot to search for a target object from a shelf and a method for selecting empty spaces to move off-target objects. The proposed method is based on two recognition models to select a space and plan motions. The object is to be moved and the space is selected based on the size and position of empty spaces. In the experiments, the robot planned actions to search for a target object from a shelf and to place an object near a group of similar objects. We used these in the RoboCup@Home competition to evaluate the effectiveness of the proposed method.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149480150&origin=inward

Pose Detection for Flexible-Indefinite Objects using Pseudo-Bone Data 査読有り

Yoshimoto Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics   399 - 401   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes a method for recognizing the poses of a flexible-indefinite object. Some flexible-indefinite objects, such as fried shrimp, differ between individuals. Therefore, it is difficult to estimate the pose of these foods by point cloud fitting or other methods. We propose "pseudo-skeleton" data for these objects. Pseudo-skeleton data consists of "key-points," which are joints, and "bones," which connect between key-points. For example, fried shrimp are given 3 key-points; "head," "belly," and "tail." In addition, the bones that connect them are given. In the experiment, objects pose recognition model based on the human pose recognition model trains pseudo-skeleton data of fried shrimp. We confirmed that the model estimates the poses in the images.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149467263&origin=inward

Random network device fabricated using Ag<inf>2</inf>Se nanowires for data augmentation with binarized convolutional neural network 査読有り

Kotooka T., Tanaka Y., Tamukoh H., Usami Y., Tanaka H.

Applied Physics Express   16 ( 1 )   2023年01月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

An Ag2Se nanowire random network was fabricated for application as a data augmentation device and combined with a binary convolutional neural network (BCNN) to achieve high accuracy in voice classification tasks. Due to the nonlinear high-dimensional characteristics resulting from the formation of the conductive filament at the cross junction, the Ag2Se device could transform input data into higher-order multiple signals, thereby enhancing the accuracy of the classification task by augmenting input signals. The results indicate that materials can realize data augmentation with the same performance as software, suggesting that material-based hardware can be used as an elemental technology for information processing.

DOI： 10.35848/1882-0786/acae6a

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146494177&origin=inward