研究者詳細 - 田向　権

論文 - 田向　権

分割表示376 件中 41 - 60 件目／全件表示 >>

A Rapidly Adjustable Object Recognition System through Language Based Prompt Engineering 査読有り

Yamaguchi N., Shiba T., Isomoto K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 425 - 429 2024年01月

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 the use of language-based prompt engineering to achieve rapidly adjustable object recognition in RoboCup@Home. The proposed prompt engineering involves humans adding features, such as the color and material of an object, into the text prompts inputted into Language Segment Anything. In this research, we evaluated the effectiveness of our proposed method in three benchmark tests for object recognition at RoboCup@Home 2023. The results show that the highest scores were obtained in specific tasks, indicating that the proposed method applies to various recognition tasks.

Kyutacar

Scopus

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A Low Computational Cost Hand Waving Action Recognition System with Echo State Network for Home Service Robots 査読有り

Yamaguchi H., Mizutani A., Syulistyo A.R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 421 - 424 2024年01月

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 proposes a low computational cost hand-waving action recognition system for non-verbal communication in home service robots. The system is based on an echo state network, which requires lower computational costs than that of deep neural networks (DNNs), and processes time-series data of skeletal coordinates of humans to recognize hand-waving actions. Additionally, this study proposes and compares two types of Preprocessing ing methods of the skeletal coordinates to ensure the robustness of the human positions on the frame: one method extracts shoulder and arm angles, which are invariable regardless of the humans’ positions and the other normalizes the skeletal coordinates. The experimental result shows that the proposed system has competitive accuracy and is robust to varying human positions.

Scopus

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Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers 査読有り

Hirayae S., Yoshioka K., Yokota A., Kawashima I., Tanaka Y., Katori Y., Nomura O., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024年01月

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）

To use reservoir computing (RC) for practical tasks, both a high memory capacity and nonlinearity are required; however, some RC models have the problem of a low memory capacity. We propose a delay mechanism for increasing the memory capacity in RC as well as a simple and small-scale digital circuit for implementing the delay mechanism. The proposed delay mechanism is integrated into the input layer of the RC model and is expected to be implemented in several RC models, such as material reservoirs and chaotic Boltzmann machine (CBM)-RC. We conducted experiments using a CBM-RC with a delay mechanism (CBM-RC-DL) and evaluated the performance improvement achieved by introducing a delay mechanism. We used CBM-RC as the base model because it is an appropriate model for the hardware implementation of large networks but has a low memory capacity. The experimental results for CBM-RC-DL indicated that the delay mechanism significantly increased the memory capacity of CBM-RC with the addition of a small-scale circuit. Furthermore, the entire synthesized CBM-RC-DL was sufficiently small-scale to be implemented in a field-programmable gate array for edge computing, and it outperformed conventional methods in nonlinear autoregressive moving average 10 (NARMA10) - a benchmark task for time-series data processing. The proposed delay mechanism can facilitate the use of many RC models because of its simple structure.

DOI： 10.1109/ISCAS58744.2024.10557841

Scopus

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FPGA Implementation for Large Scale Reservoir Computing based on Chaotic Boltzmann Machine 査読有り

Matsumoto S., Ichikawa Y., Kajihara N., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024年01月

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 reports on a field programmable gate array (FPGA) implementation of Chaotic Boltzmann Machine Reservoir Computing (CBM-RC). The reservoir will be large-scale, as it is expected to be applied to sensor information prediction for autonomous mobile robots. Therefore, we employ a design premised on storing the weight information into a large memory outside the FPGA. We propose an efficient compression method for the weight matrix and a parallel processing system, by considering both the characteristics of CBM-RC and the fact that the weight matrix of a large-scale reservoir is generally a sparse matrix. Our RC system, which has more than 8000 neurons and 1024 inputs/outputs, has been implemented on an AMD Alveo U50 FPGA board. This RC is the largest scale compared to those in related studies. We have performed the NARMA10 task and demonstrated that we can estimate 1024 predictions at once with NMSE accuracy that is even or better to conventional RC.

DOI： 10.1109/ISCAS58744.2024.10558106

Scopus

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Unified Understanding of Environment, Task, and Human for Human-Robot Interaction in Real-World Environments 査読有り

Yano Y., Mizutani A., Fukuda Y., Kanaoka D., Ono T., Tamukoh H.

IEEE International Workshop on Robot and Human Communication, RO-MAN 224 - 230 2024年01月

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

To facilitate human-robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map optimizes robot behavior by managing an occupancy grid map and dynamic information, such as furniture and humans, in separate layers. The task understanding system targets tasks that require multiple actions, such as serving ordered items. Task representations that predefine the flow of necessary actions are applied to achieve highly accurate understanding. The response generation system is executed in parallel with task understanding to facilitate smooth HRI by informing humans of the subsequent actions of the robot. In this study, we focused on waiter duties in a restaurant setting as a representative application of HRI in a dynamic environment. We developed an HRI system that could perform tasks such as serving food and cleaning up while communicating with customers. In experiments conducted in a simulated restaurant environment, the proposed HRI system successfully communicated with customers and served ordered food with 90% accuracy. In a questionnaire administered after the experiment, the HRI system of the robot received 4.2 points out of 5. These outcomes indicated the effectiveness of the proposed method and HRI system in executing waiter tasks in real-world environments.

DOI： 10.1109/RO-MAN60168.2024.10731235

Scopus

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Robust Binary Encoding for Ternary Neural Networks Toward Deployment on Emerging Memory 査読有り

Fuengfusin N., Tamukoh H., Nomura O., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

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）

Deep neural networks (DNNs) have enabled state-of-the-art performance across various applications. However, their deployment is often hindered by high energy demands. One solution is to deploy DNNs on hardware equipped with emerging non-volatile memory, which does not require energy to maintain memory state. Nonetheless, this approach may introduce bit-flips in DNN parameters, leading to a drop in model performance. To mitigate this issue, ternary neural networks (TNNs), which are known for their robustness against bit-flips, can be utilized. To accelerate TNNs, it is necessary to define binary representations (BRs) for ternary values to enable bit-wise operations. This paper proposes a framework for identifying a set of BRs that minimizes the discrepancy between the Top-1 Accuracy of TNNs before and after bit-flips, thereby enhancing their robustness. The BRs are evaluated using randomly generated data, the CIFAR-10 dataset, and the ImageNet 2012 dataset. The experimental results demonstrate that our BRs can significantly reduce or maintain the Top-1 Accuracy degradation caused by bit-flips, compared to conventional BRs.

DOI： 10.1109/IJCNN60899.2024.10650030

Scopus

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Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor 査読有り

Tokuno Shoshi, Kimizuka Kouki, Tanaka Yuichiro, Usami Yuki, Tanaka Hirofumi, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 15 ( 4 ) 883 - 898 2024年01月

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>This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate.</p>

DOI： 10.1587/nolta.15.883

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/nolta/15/4/15_883/_pdf

CMOS digital-analog mixed signal VLSI implementation of a hippocampus-inspired model 査読有り

Shishido Y., Nomura O., Tateno K., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

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）

For artificial intelligence (AI) to be useful in the home, it is required to acquire unique knowledge of the home obtained through interaction with space and environment. This is difficult for deep learning-based AI. The human brain can learn unique knowledge from few experiences. The entorhinal cortex and hippocampus play essential roles for episodic memory formation and recall. While entorhinal-hippocampal models have been proposed that can reproduce episodic memory, hardware systems that implement such models face challenges related to high computational complexity, power consumption, and processing speed. In this paper, we propose a digital-analog mixed-signal CMOS VLSI implementation of a hippocampus-inspired model that can memorize and associate place and object information essential for the formation of episodic memory. By using both analog and digital in-memory computing architecture, the proposed circuit has achieved a computational efficiency of 22 TOPS/W, which is very high for AI hardware with a learning function. The proposed circuit was fabricated, measured, and evaluated. The results of an experiment using a fabricated chip and a control system showed that the proposed circuit can memorize and process place and object information, and can acquire environment-unique knowledge through interaction with a space.

DOI： 10.1109/IJCNN60899.2024.10650772

Scopus

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An efficient spatial representation scheme for memory-based hippocampus-inspired model with VLSI implementation 査読有り

Shishido Yuka, Nomura Osamu, Tateno Katsumi, Tamukoh Hakaru, Morie Takashi

Nonlinear Theory and Its Applications, IEICE ( 一般社団法人電子情報通信学会 ) 15 ( 4 ) 785 - 795 2024年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

<p>Hippocampus integrates events and places information critical for episodic memory. Memory-based hippocampus-inspired model (MBHIM) is a hippocampus-inspired model suitable for VLSI implementation. This paper proposes an efficient spatial representation scheme for MBHIM with VLSI implementation. Each layer represents a space of a different size to represent and store spatial information at different resolutions. This scheme can achieve the representation of large spaces with high spatial resolutions, even when the number of memory cells per layer is limited. We conducted an experiment using a VLSI system of MBHIM to demonstrate the effectiveness of the proposed scheme.</p>

DOI： 10.1587/nolta.15.785

CiNii Research

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001317

A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information 査読有り

Mizutani A., Tanaka Y., Tamukoh H., Nomura O., Tateno K., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024年01月

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 acquire environment-specific knowledge through experiences (episodes) in a home to perform tasks autonomously. We propose a hippocampus-inspired memory system comprising an environment-specific knowledge module that handles episodic memory and a common knowledge module. The robot works in a home and needs to learn the locations of objects in the home with minimal user help to deliver or store objects. We employ a large language model (LLM) that runs on an edge device as the common knowledge module to protect the privacy of users. The environment-specific knowledge module acquires episodes and generates contextual information. The LLM receives contextual information generated by the environment-specific knowledge module, with which it infers an appropriate possible location. We verified that the LLM could infer the possible object locations and that the proposed system could minimize the required user effort.

DOI： 10.1109/IJCNN60899.2024.10651337

Scopus

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ホームサービスロボットのためのリザバーコンピューティングによる低計算コストの手振り認識システム

山口紘正，水谷彰伸，アリラフマードシュリスティヨ，田中悠一朗，田向権

第25回日本知能情報ファジィ学会九州支部学術講演会 2023年12月

曖昧な言語指示に対応可能な大規模言語モデルを用いた動作計画システムの開発査読有り

山尾晃世, 金岡大樹, 磯本航世, 田向権

人工知能学会第二種研究会資料 ( 一般社団法人人工知能学会 ) 2023 ( Challenge-063 ) 04 2023年11月

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）

人の言語指示から適切な動作を行う汎用的なサービスロボットの実現のためには，高精度な動作計画が必要となる．大規模言語モデルを活用したSayCanと呼ばれる動作計画システムは非常に高精度であるが，いくつかの問題を有している．我々は，SayCanの有する問題の中でも，具体的な対象を特定できない抽象名詞を含む曖昧な言語指示に対して，動作計画の精度が低下する問題と，システムが持つスキルの数に伴い推論時間が増加する問題に注目した．我々は，SayCanをベースとし，抽象名詞を聞き返す機能とルールベースでスキルを抽出する機能を導入した動作計画システムを提案する．提案システムは，言語指示の解釈を容易にし，適切な動作計画を可能とした．また，RoboCup@Homeと呼ばれるホームサービスロボットの性能を評価する競技会において高い成績を獲得することにより，実環境下でも十分動作することを示した．

DOI： 10.11517/jsaisigtwo.2023.challenge-063_04

CiNii Research

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001130

曖昧な言語指示に対応可能な動作計画システムの開発

山尾晃世，金岡大樹，磯本航世，田向権

第63回人工知能学会 AIチャレンジ研究会，SIG-Challenge-063-04 2023年11月

Calculate a Destination for Communicating with a Customer in Unknown Environments 査読有り

Yuga Yano, Kazuma Sakakibara and Hakaru Tamukoh