研究者詳細 - 田向　権

論文 - 田向　権

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人とロボットが一緒に暮らす未来の空間創り査読有り

水谷彰伸, 伊東啓太郎, 石塚直登, 須藤朋美, 田中啓文, 田中悠一朗, 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 ) 43 ( 4 ) 367 - 370 2025年01月

Towards Optimizing Physical Reservoir Computing: A Hybrid PCPO-ESN Framework for Time Series Forecasting 査読有り

Pramanta D., Fuengfusin N., Syulistyo A.R., Tamukoh H.

Proceedings IEEE International Symposium on Circuits and Systems 2025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）

In neuroscience-inspired computing, reservoir computing (RC) has emerged as a powerful model that leverages recurrent dynamics to mimic neural systems. Echo State Networks (ESNs), a prominent RC framework, are effective for modeling temporal data. However, physical RC using pulse-coupled phase oscillator (PCPO) networks, while promising for hardware systems, often struggle with flexibility, limiting their performance in time series forecasting. To address these challenges, this paper introduces a novel hybrid RC framework that combines the both states from ESNs and PCPO using two oscillators towards optimization physical RC. This hybrid-state using the oscillating events and enhance the representational capacity of the reservoir, thereby enabling the model to learn pertinent features for more accurate predictions. We evaluate our framework using the synthetic NARMA-10 dataset and the real-world Numenta Anomaly Benchmark (NAB). Experimental results show that the proposed method achieves lower mean squared error (MSE) values 7.19×10<sup>-3</sup> on NARMA-10 and 33.60×10<sup>-3</sup> on NAB, which outperforming the baseline ESN model.

DOI： 10.1109/ISCAS56072.2025.11044145

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Simple open-set recognition combining metric learning and anomaly detection 査読有り

Kanaoka D., Tanaka Y., Tamukoh H.

Advanced Robotics 39 ( 7 ) 368 - 382 2025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（学術雑誌）

In real world scenarios, there is always a risk that an object recognition system will encounter unknown objects. Open-set recognition, which identifies such objects as ‘unknown,’ is a crucial research area. Most existing open-set recognition methods have complex structures. Considering the application of closed-set recognition models already used in the real world are utilized for open-set recognition, a simple structure is preferable for implementation. In this study, we propose a novel open-set recognition method that focuses on the feature space of a recognition model by combining metric learning and anomaly detection (AD). By applying metric learning, the feature vectors of the same class are clustered and those of different classes are separated, increasing the open space for the detection of unknown objects. The AD model trains on feature vectors from the intermediate layers of a recognition model, allowing unknown detection without altering the existing model structure. We evaluated various combinations of metric learning and AD techniques, identifying an effective pairing. Our results show that the proposed method performs comparably to state-of-the-art approaches on simple datasets. The simplicity of the proposed method makes it easy to adapt existing closed-set recognition models for open-set recognition in real-world applications.

DOI： 10.1080/01691864.2025.2486394

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Robotic Grasping of Common Objects: Focusing on Edge Detection for Improved Handling 査読有り

Shiba T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 487 - 490 2025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）

Grasping objects like plates and cups poses unique challenges for robots because of their irregular shapes and the difficulty of finding reliable grasp points. Traditional approaches often attempt to grasp the object at its center, but this strategy tends to fail for items like plates or cups, whose shapes deviate from simple forms like cubes or spheres. To address this issue, we propose a new method that utilizes AI-powered image analysis to identify the best edges for grasping. Through experiments conducted with a home service robot and a set of YCB objects, we evaluated the effectiveness of our approach compared to conventional methods. The results revealed a significant improvement in the success rate, particularly for objects with prominent edges, such as cups.

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Reservoir Computing with VCO-Based Spiking Neurons for Regression and Classification 査読有り

Yoshioka K., Allred P., Barton T., Sahoo B.D., Kuan Y.C., Chiang S.H.W., Tamukoh H.

Proceedings IEEE International Symposium on Circuits and Systems 2025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）

Reservoir computing (RC) significantly reduces the requirement on hardware and training resources, making it suitable for edge-computing applications. This work proposes using voltage-controlled oscillator (VCO)-based spiking neurons for RC to leverage the intrinsic randomness and variability of the neuron circuit for low-power operations. We describe the underlying circuit design and propose a network architecture based on the spiking neuron for RC. We demonstrate the effectiveness of the proposed RC network using VCO-based spiking neurons through benchmark tasks and electrocardiogram (ECG) classification.

DOI： 10.1109/ISCAS56072.2025.11043229

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Proposal of a Grasp Verification Method Utilizing Background Subtraction and Depth Information 査読有り

Terashima R., Yano Y., Arimura K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 491 - 494 2025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）

Commonly grasp verification approach involves using the opening width of the robot's gripper. However, methods based on the opening width of the gripper may not apply to slender objects. In this study, we propose a grasp verification method using background subtraction. Our proposed method uses depth information to mask the background, isolating only the images of the gripper and the grasped object. Subsequently, a difference image is created by comparing the current image with the pre-grasp state, and the grasp state is detected based on the magnitude of the observed changes. The method minimizes environmental influences by masking the background, enabling highly accurate grasp verification even for complex objects. Through experiments, we validate the effectiveness of the proposed method.

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Prediction of Timing and Amount of Houseplants Watering by an Echo State Network on Jetson 査読有り

Yoshimura W., Arimura K., Kobayashi R., Mizutani A., Fujino T., Tanaka Y., Sudo T., Ishizuka N., Ito K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 529 - 534 2025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）

Cultivating of houseplants in biophilic designed spaces requires appropriate timing and amount of watering. However, determining them is challenging, as fluctuations in ambient temperature can influence these factors. We develop a system capable of predicting ambient temperature changes and determining the appropriate timing and amount of watering. The system acquires ambient data using sensors connected to a Jetson Nano and processes the data using a neural network for the prediction and determination. We adopt an echo state network, a lightweight neural network, enabling a power-efficient system capable of running on edge devices. Additionally, we implement a function to notify the user of the timing and amount of watering via a chat service whenever the soil moisture content drops below a predefined threshold.

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Optimizing Object Placement for Human Support Robots Using a Two-dimensional Irregular Packing Algorithm for Efficient Tray Storage 査読有り

Buttawong N., Isomoto K., Yamao K., Fuengfusin N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 471 - 475 2025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）

Human support robots (HSR) are robots that assist humans in their daily tasks. Their main application is tidying up, which involves detecting objects, determining appropriate placement locations, and organizing them. This study focuses on tidying up tray storage. Determining suitable storage positions is essential for storing objects in the tray. If the robot store objects in fixed predetermined locations, it can lead to inefficient use of storage space, and in the worst case, objects might collide and overflow from the tray. To address this limitation, we propose a 2-dimensional irregular packing algorithm utilizing an object mask method to calculate the best placement location. This study evaluates the proposed packing algorithm against the standard method to determine which approach is more effective in HSR applications.

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Object Positions Interpretation System for Service Robots Through Targeted Object Marking 査読有り

Yamao K., Kanaoka D., Isomoto K., Tamukoh H.

2025 IEEE SICE International Symposium on System Integration Sii 2025 998 - 1003 2025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）

Service robots are typically required to interpret and execute various complex tasks in home environments. Recognizing the environment, such as furniture, and understanding the relationships between object positions is critical for executing various tasks. Set of mark (SoM) is a visual prompting method that focuses on interpreting the relationship between semantic regions by overlaying marks in each region. However, SoM marks segmented regions that are not objects such as walls and floors. This marking creates noise when interpreting object positions. To address this problem, we propose a novel object-position interpretation system that combines an object detection model and a vision-language model (VLM). The proposed system incorporates an object detection model to mark only objects, allowing the VLM to efficiently interpret object positions. Furthermore, the proposed system improves the accuracy of the system by including the original image and label output by the object detection model in the input to the VLM. The experimental results show that the proposed system outperforms SoM in terms of interpreting object positions.

DOI： 10.1109/SII59315.2025.10870994

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Lightweight Hand-Waving Action Recognition Using Reservoir Computing in a Cafeteria Environment 査読有り

Isomoto K., Fumoto S., Kobayashi R., Tanaka Y., Tamukoh H.

2025 IEEE SICE International Symposium on System Integration Sii 2025 1034 - 1040 2025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）

Owing to the global labor shortage and increasing need for operational efficiency, the adoption of service robots is advancing rapidly. These robots must recognize human action to understand human intention and respond appropriately based on that understanding. The action recognition systems embedded in robots need to be lightweight to operate efficiently with limited computational resources. Reservoir computing (RC) is one of the solutions for lightweight action recognition systems. Yamaguchi et al. proposed an RC-based hand-waving recognition system; however, the system cannot process multiple persons simultaneously and works only when one person is in the image. Therefore, this study proposes a lightweight hand-waving recognition system that integrates OpenPose, StrongSORT, and RC to work in complex environments with multiple individuals. Experimental results demonstrated the effectiveness of the proposed system in processing multiple people simultaneously in a crowded environment and accurately recognizing hand-waving actions with 90.75% accuracy. We also confirmed that the proposed system can process data at 24-26 FPS. We demonstrated that the proposed system can perform real-time processing. In addition, the robot with the proposed system recognized hand-waving actions in the "Restaurant"task of RoboCup@Home 2024 and obtained the second-place score.

DOI： 10.1109/SII59315.2025.10871098

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Integrating Advance Speech Recognition and Human Attribute Detection for Enhanced Receptionist Tasks in RoboCup@Home 査読有り

Arimura K., Yano Y., Kawabata T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 480 - 483 2025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）

RoboCup@Home is held to integrate service robots into society. It includes a task called “Receptionist” that evaluates Human-Robot Interaction. In this task, a robot must ask guests for their names and favorite drinks and guide them to available seats. Additionally, the robot must introduce the guest’s features, such as their clothing, to others. We developed a system integrating speech recognition and human attribute detection to achieve these functions. The robot can determine which seat a person is sitting in by detecting the person's skeletal coordinates. Additionally, the robot can identify individuals by recognizing human attributes. To verify the effectiveness of the developed system, we participated in the Receptionist task at RoboCup@Home 2024. We won first place in our league and demonstrated the effectiveness of our system.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219523493&origin=inward

Hibikino-Musashi@Home RoboCup@Home DSPL Champion 2024 査読有り

Mizutani A., Isomoto K., Yamao K., Kobayashi R., Fumoto S., Arimura K., Yamaguchi N., Shiba T., Kimizuka K., Ohno Y., Terashima R., Yamaguchi H., Fujino T., Maruno R., Yoshimura W., Mine K., Phu Thien Nhan T., Yano Y., Tanaka Y., Nishida T., Morie T., Tamukoh H.

Lecture Notes in Computer Science 15570 LNAI 461 - 472 2025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）

This paper provides an overview of the techniques employed by Hibikino-Musashi@Home during their participation in the Domestic Standard Platform League (DSPL). The team developed a robust perception system, navigation system, and task planner, as well as demonstrated their ability to secure first place in the DSPL. In particular, this paper summarizes the technologies employed in the competition.

DOI： 10.1007/978-3-031-85859-8_39

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Grasp Point Estimation using Simulator-Generated Datasets Including Pose Information 査読有り

Maruno R., Shiba T., Yamaguchi N., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 495 - 498 2025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）

We develop a system that automatically generates training datasets for object recognition models using a simulator. In this study, we incorporate pose information into the dataset. Using this system, we develop a method for estimating grasp points for objects that are difficult for robots to grasp, selecting a toy airplane as the target object. Three specific points are assigned to the object: the front, center, and back. In the grasp point estimation process, the center point is designated as the grasp point. The robot’s arm achieves an appropriate grasp by moving perpendicularly to the line connecting the front and back points toward this grasp point.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219567407&origin=inward

Efficient Object Detection with Color-Based Point Prompts for Densely Packed Scenarios in WRS FCSC 2024 査読有り

Yamaguchi N., Shiba T., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 509 - 512 2025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）

This paper introduces a novel object detection method designed for densely packed environments, such as those encountered in the World Robot Summit Future Convenience Store Contest (FCSC) 2024. Our system leverages color-based point prompts in conjunction with Segment Anything (SAM) 2 to achieve precise object segmentation and grasp point estimation, specifically targeting scenarios where objects like rice ball cluster tightly within containers. Unlike traditional methods that depend on pre-defined grasping strategies susceptible to positional drift in mobile robots, our approach dynamically identifies and isolates objects without requiring extensive retraining inherent to CNN or Transformer models. We conducted comprehensive experiments comparing our method against SAM 2 and Grounding DINO using a dataset of 10 test images containing 202 rice balls. Additionally, we deployed the system on a Toyota Human Support Robot during the FCSC Stock Task to assess real-world performance metrics. Results demonstrate that our method achieves higher detection accuracy and operational efficiency, validating its potential for autonomous retail applications.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219545570&origin=inward

Dynamic Threshold Spatial-Temporal Filter on FPGAs for Event-based Vision Sensors 査読有り

Toyoda R., Yoshioka K., Tamukoh H.

2025 IEEE SICE International Symposium on System Integration Sii 2025 854 - 859 2025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）

Event-based vision sensors are high-speed, wide dynamic range image sensors with potential applications in domains such as robotics and visual navigation. However, these sensors are sensitive to noise, particularly under low-light conditions, degrading the data quality. Therefore, developing a filter capable of detecting and removing noise from different sources with high accuracy is crucial. Moreover, removing near-edge noise in high-density areas is particularly challenging using conventional methods because of their high spatial-temporal correlation with actual events. We propose a dynamic threshold spatial-temporal filter that detects high- or low-density areas and removes noise. Detection was achieved by counting the number of events occurring within a certain period in the area surrounding each event. Applying an appropriate threshold for each density significantly enhanced noise processing accuracy, as reflected by the mean square error and peak signal-to-noise ratio metrics. Moreover, we synthesize digital circuits in a field-programmable gate array and demonstrated a notable reduction in processing time compared to that of the central processing unit-based approach, achieving up to 74-fold faster in processing speed. These findings suggest that the proposed filter can significantly enhance real-time event-based vision systems, particularly in environments with varying noise conditions.

DOI： 10.1109/SII59315.2025.10870589

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Developing a Sound-Based Method to Synchronize Multiple Videos Recorded by Multiple Sound Sources 査読有り

Jargal D., Kato R., Taniguchi T., Shibata K., Koga T., Al Aama O., Tamukoh H., Wagatsuma H.

Proceedings of International Conference on Artificial Life and Robotics 809 - 814 2025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）

The surround environmental monitoring from a vehicle on the road is important for the driving behavior analysis as well as the monitoring of driver’s operations. A light weight mobile camera is useful for the record of multiple directions from the vehicle simultaneously. However, the timing synchronization is an important issue need to be solved. In this purpose, we proposed the sound-based method to synchronize different videos recorded with environmental sounds. In this task, the extraction of common sound features and amplifying of the features are necessary to superimpose those sound profiles to find consisting time points. In the validation the effectiveness, we used recoded videos from the bus driven by an expert driver.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219506499&origin=inward

Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors 査読有り

Rakheja A., Saini R., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 484 - 486 2025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）

This paper presents a human action classification system using a spiking neural network (SNN) and an event-based vision sensor (EVS). The EVS captures asynchronous data with high temporal resolution, wide dynamic range, and motion blur resistance. SNNs, inspired by biological neurons, process this data event-driven, ensuring energy efficiency, low latency, and scalability. Using a custom EVS dataset of 600 videos across four action types and the optical flow for feature extraction, the system achieved 93% accuracy, offering an efficient solution for action recognition.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219530000&origin=inward

Classification of Human Activity by Event-based Vision Sensors using Echo State Networks 査読有り

Saini R., Rakheja A., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 476 - 478 2025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）

We propose a system for human activity recognition using an event-based vision sensor (EVS) with echo state networks (ESNs). Conventional cameras are susceptible to motion blur and require computationally intensive methods, whereas EVS provides no motion blur and low latency. Our research aims to enable accurate recognition of human activities by using energy-efficient methods. Therefore, we adopt ESNs, which require low computational costs, for the classifier. Additionally, we use feature extraction algorithms such as optical flow and histogram of gradients to improve accuracy. We used an EVS activity recognition dataset created by us containing six human activities and 600 videos. The results showed that our hybrid approach outperformed several techniques. We achieved 89% accuracy when trained with ridge regression.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219518069&origin=inward

Basic Research on the Development of Space Standards for the Use of Service Robots in Housing Using the Urban Renaissance Agency's Housing Complex 査読有り

Matsuoka R., Nonoshita K., Ishizuka N., Kobayashi R., Mizutani A., Tamukoh H., Tanaka H.

Proceedings of International Conference on Artificial Life and Robotics 519 - 522 2025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）

This study conducts basic research on developing space standards for integrating home service robots in residences, using a room in an housing complex from Urban Renaissance Agency as a model. The study compares these spaces with existing robot mobility standards to identify issues. The robot-friendly level of the room was evaluated using RFA standard. Results showed that most rooms fell into Level C, the lowest of three levels, in areas such as “fixture width” and “steps.” Furthermore, when operating two types of home service robots, the study uncovered unique robot behavior issues in the housing that were not anticipated by RFA standard.

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85219573724&origin=inward

Application of AI Robot Technology for Biophilic Design 査読有り

Manabe K., Miyazono R., Ito K., Sudo T., Ishizuka N., Mizutani A., Tamukoh H., Tanaka Y., Ueda E., Anamizu Y., Furuya L., Tamai H., Nakano S., Tanaka H.

Proceedings of International Conference on Artificial Life and Robotics 513 - 518 2025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 enhance human health and well-being, Biophilic design has been increasingly recognized in recent years. This design is characterized by the integration of natural elements such as plants, nature light, and water into spaces. However, challenges are posed by the maintenance of live plants, as their decline can be caused by insufficient environmental conditions. The aim of this study is to propose a robotic system of autonomously relocate houseplants to environments optimized based on sensor data, including light, temperature, and humidity. Through the integration of AI robotics with ecological design principles, it is aimed to enhance sustainability and redefine the relationship between humans, nature, and technology, fostering a harmonious interaction among "robots, nature, and humans.".

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