Details of a Researcher - SUGIURA, Komei

Papers - SUGIURA, Komei

Division display >> ／ All the affair displays 1 - 211 of about 211

Mobile Manipulation Instruction Generation From Multiple Images With Automatic Metric Enhancement

K Katsumata, M Kambara, D Yashima, R Korekata, K Sugiura

IEEE Robotics and Automation Letters 2025

DM2RM: dual-mode multimodal ranking for target objects and receptacles based on open-vocabulary instructions

R Korekata, K Kaneda, S Nagashima, Y Imai, K Sugiura

Advanced Robotics, 1-16 abs/2408.07910 2025

Access to Document (DOI)

NaiLIA: 緩和損失に基づくネイルデザインのマルチモーダル検索

雨宮佳音，小松拓実，八島大地，是方諒介，勝又圭，杉浦孔明

人工知能学会全国大会論文集第 39 回 (2025), 2Win555-2Win555 (The Japanese Society for Artificial Intelligence) JSAI2025 ( 0 ) 2Win555 - 2Win555 2025

We focus on the task of retrieving nail design images based on dense intent descriptions, which represent long and multi-layered user intent for nail designs. This is challenging because such descriptions specify flexibly created paintings and pre-manufactured embellishments, as well as visual characteristics, spatial relationships, higher-order themes, and overall impressions. Existing vision-and-language foundation models often struggle to capture the interplay between paintings and embellishments, failing to incorporate multi-layered intent descriptions. To address this, we propose NaiLIA, a method that enables the retrieval of nail design images that comprehensively align with descriptions with dense user intent. Our approach estimates confidence scores for images that align with a given description and can be considered as positive examples but are not explicitly labeled (unlabeled positives), and incorporates this score into the loss function. To evaluate NaiLIA, we constructed a benchmark consisting of 10,625 images collected from people with diverse cultural backgrounds. The images were annotated with long and dense intent descriptions given by over 200 annotators. Experimental results demonstrate that the proposed method outperforms standard methods by 20.9 points in terms of recall@10.

Interactive robot action replanning using multimodal llm trained from human demonstration videos

C Hori, M Kambara, K Sugiura, K Ota, S Khurana, S Jain, R Corcodel, ...

ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and … 1 - 5 2025

Access to Document (DOI)

Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images

S Nagashima, K Sugiura

Proceedings of the IEEE/CVF International Conference on Computer Vision … abs/2508.07847 2025

Access to Document (DOI)

Crosslingual Visual Prompt に基づくテキスト付き画像からの日常物体検索

戸倉健登，是方諒介，小松拓実，今井悠人，杉浦孔明

人工知能学会全国大会論文集第 39 回 (2025), 1Win452-1Win452 (The Japanese Society for Artificial Intelligence) JSAI2025 ( 0 ) 1Win452 - 1Win452 2025

This study explores a task where a robot searches for images containing target objects based on user language queries from a large set of images captured in diverse indoor and outdoor environments. Both images with and without scene text are considered. For example, when searching with the query, "Pass me the red container of Sun-Maid raisins on the kitchen counter," the model ranks images containing a container labeled "Sun-Maid raisins" on the kitchen counter higher. However, linking visual semantics with scene text is challenging. Additionally, multimodal search requires large-scale, high-speed inference, making it impractical to rely solely on a multimodal large language model (MLLM). To address this, we introduce a Scene Text Visual Encoder, integrating an Aligned Representation with a narrative representation obtained using an MLLM based on Crosslingual Visual Prompting. Incorporating OCR results into the prompt further reduces hallucination. Experiments show that the proposed method outperforms multimodal foundation models across multiple benchmarks in standard evaluation metrics for ranking-based learning.

Pre-Manipulation Alignment Prediction for Open-Vocabulary Object Manipulation Based on End-Effector Trajectories

M Kambara, K Sugiura

2025 19th International Conference on Machine Vision and Applications (MVA), 1-5 1 - 5 2025

Access to Document (DOI)

Pre-manipulation alignment prediction with parallel deep state-space and transformer models

M Kambara, K Sugiura

Advanced Robotics 39 (13), 806-816 2025

Object Segmentation from Open-Vocabulary Manipulation Instructions Based on Optimal Transport Polygon Matching with Multimodal Foundation Models.

Takayuki Nishimura, Katsuyuki Kuyo, Motonari Kambara, Komei Sugiura

IROS 9549 - 9556 2024

Access to Document (DOI)

Trimodal Navigable Region Segmentation Model: Grounding Navigation Instructions in Urban Areas

N Hosomi, S Hatanaka, Y Iioka, W Yang, K Kuyo, T Misu, K Yamada, ...

IEEE Robotics and Automation Letters 9 (5), 4162-4169 2024

Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine.

Kanta Kaneda, Shunya Nagashima, Ryosuke Korekata, Motonari Kambara, Komei Sugiura

9 ( 3 ) 2088 - 2095 2024

Access to Document (DOI)

Co-scale cross-attentional transformer for rearrangement target detection

H Matsuo, S Ishikawa, K Sugiura

Advanced Robotics 38 (18), 1277-1286 2024

Cooperative Control of Multiple CAs

T Nagai, T Nakamura, K Sugiura, T Taniguchi, Y Suzuki, M Hirata

Cybernetic Avatar, 151-207 2024

Deneb: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning

K Matsuda, Y Wada, K Sugiura

Proceedings of the Asian Conference on Computer Vision, 3570-3586 2024

Layer-Wise Relevance Propagation with Conservation Property for ResNet

S Otsuki, T Iida, F Doublet, T Hirakawa, T Yamashita, H Fujiyoshi, ...

European Conference on Computer Vision, 349-364 2024

Mask-Attention A3C: Visual Explanation of Action-State Value in Deep Reinforcement Learning

H Itaya, T Hirakawa, T Yamashita, H Fujiyoshi, K Sugiura

IEEE Access 12 86553 - 86571 2024

Access to Document (DOI)

Multimodal Target Localization with Landmark-Aware Positioning for Urban Mobility

N Hosomi, Y Iioka, S Hatanaka, T Misu, K Yamada, N Tsukamoto, ...

IEEE Robotics and Automation Letters 10 ( 1 ) 716 - 723 2024

Access to Document (DOI)

Nearest neighbor future captioning: generating descriptions for possible collisions in object placement tasks

T Komatsu, M Kambara, S Hatanaka, H Matsuo, T Hirakawa, T Yamashita, ...

Advanced Robotics 38 (18), 1265-1276 2024

Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning With Dense Labeling

D Yashima, R Korekata, K Sugiura

IEEE Robotics and Automation Letters 2024

Mask-Attention A3C: Visual Explanation of Action–State Value in Deep Reinforcement Learning

H Itaya, T Hirakawa, T Yamashita, H Fujiyoshi, K Sugiura

IEEE Access 12, 86553-86571 2024

Layer-Wise Relevance Propagation for ResNet: 保全公理を満たす視覚的説明生成

小槻誠太郎，飯田紡，平川翼，山下隆義，藤吉弘亘，杉浦孔明

人工知能学会全国大会論文集第 38 回 (2024), 4I1GS703-4I1GS703 (The Japanese Society for Artificial Intelligence) JSAI2024 ( 0 ) 4I1GS703 - 4I1GS703 2024

In the modern era, where deep learning is applied across a wide range of fields, the explainability of deep learning models is crucial, and the generation of explanations with high transparency is desirable. Layer-wise Relevance Propagation (LRP) is mentioned as a method for generating explanations by backpropagating relevance scores, offering high transparency. However, this method has been applied only to models without skip connections, as its application to models with skip connections does not satisfy the conservation property, resulting in poor quality explanations. Therefore, this paper proposes a method for calculating the backpropagation of relevance scores that satisfies the conservation property in models with skip connections. The proposed method outperformed existing methods in terms of Insertion-Deletion Score while satisfying the conservation property.

C3-LRP: Visual Explanation Generation based on Layer-Wise Relevance Propagation for ResNet

F Doublet, S OTSUKI, I TSUMUGI, K SUGIURA

人工知能学会全国大会論文集第 38 回 (2024), 4Q1IS2c03-4Q1IS2c03 (The Japanese Society for Artificial Intelligence) JSAI2024 ( 0 ) 4Q1IS2c03 - 4Q1IS2c03 2024

In this paper, we focus on the task of visualizing important regions in an image as high-quality visual explanations of the model’s decisions with a clear theoretical background. We introduce a novel calculation method for Layer-wise Relevance Propagation (LRP) specifically tailored to models featuring skip connections such as ResNet. This method’s strength lies in its adaptability, as the backpropagation technique is distinctly defined for each layer, enhancing its extensibility. To validate our method, we conduct an experiment on the CUB-200-2011 dataset. The proposed method successfully generates appropriate explanations and, based on the Insertion-Deletion score, outperforms the baseline methods.

Alternative Adapter Model: 視覚言語基盤モデルのための視覚的説明生成

平野愼之助，飯田紡，杉浦孔明

人工知能学会全国大会論文集第 38 回 (2024), 1D3GS705-1D3GS705 (The Japanese Society for Artificial Intelligence) JSAI2024 ( 0 ) 1D3GS705 - 1D3GS705 2024

In the modern era where deep learning is applied across a wide range of fields, the explainability of models is of paramount importance. However, existing methods are not optimized for vision-language foundation models, leading to lower explanation quality for such models. Therefore, this study proposes the Alternative Adapter Model, an explanation generation model tailored to vision-language foundation models. By introducing a Side Branch Network connected to the vision-language foundation model, the proposed method extracts features suitable for explanation generation. Furthermore, by implementing the Alternative Epoch Architecture, which dynamically changes the outputs of modules and the layers to be frozen, we address the issue of overly narrow focus areas. To evaluate the proposed method, experiments were conducted using the CUB-200-2011 dataset. The results demonstrate that the proposed method surpasses existing methods in mean IoU, Insertion Score, Deletion Score, and Insertion-Deletion Score, which are standard metrics for visual explanation generation tasks.

Polos: Multimodal Metric Learning from Human Feedback for Image Captioning.

Yuiga Wada, Kanta Kaneda, Daichi Saito, Komei Sugiura

CVPR 13559 - 13568 2024

Access to Document (DOI)

Retention 機構に基づく頭蓋内脳波の分類と BMI の構築

長嶋隼矢，兼田寛大，飯田紡，田口美紗，平田雅之，杉浦孔明

人工知能学会全国大会論文集第 38 回 (2024), 4N1GS101-4N1GS101 (The Japanese Society for Artificial Intelligence) JSAI2024 ( 0 ) 4N1GS101 - 4N1GS101 2024

Speech impairments from conditions like Amyotrophic Lateral Sclerosis and muscular dystrophy severely restrict patient communication, affecting daily and social life. Decoding technology based on Electrocorticography (ECoG) is essential for supporting these patients' communication. In this study, we propose a novel architecture combining a specialized convolutional layer for electrode feature extraction and a retentive network for ECoG signal classification of motor imagery, outperforming all baselines in accuracy.

Switching Text-based Image Encoders for Captioning Images with Text

A Ueda, W Yang, K Sugiura

IEEE Access 11 55706 - 55715 2023

Access to Document (DOI)

Affective Image Captioning for Visual Artworks Using Emotion-Based Cross-Attention Mechanisms

S Ishikawa, K Sugiura

IEEE Access 11, 24527-24534 11 24527 - 24534 2023

Access to Document (DOI)

Switching Head-Tail Funnel UNITER for Dual Referring Expression Comprehension with Fetch-and-Carry Tasks

R Korekata, M Kambara, Y Yoshida, S Ishikawa, Y Kawasaki, ...

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems … 2023

Prototypical Contrastive Transfer Learning for Multimodal Language Understanding

S Otsuki, S Ishikawa, K Sugiura

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems … 2023

Multimodal Diffusion Segmentation Model for Object Segmentation from Manipulation Instructions

Y Iioka, Y Yoshida, Y Wada, S Hatanaka, K Sugiura

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems … 2023

Nearest Neighbor Future Captioning: 物体配置タスクにおける衝突リスクに関する説明文生成

小松拓実，神原元就，畑中駿平，松尾榛夏，平川翼，山下隆義，藤吉弘亘， ...

人工知能学会全国大会論文集第 37 回 (2023), 3G1OS24a05-3G1OS24a05 (The Japanese Society for Artificial Intelligence) JSAI2023 ( 0 ) 3G1OS24a05 - 3G1OS24a05 2023

The practical implementation of domestic support robots that can communicate using natural language is a promising solution for those in need of assistance. In particular, the ability to predict potential hazards associated with task execution and to prompt the user for judgment can enhance safety and convenience. However, accurate prediction is difficult because information about future events cannot be utilized. In existing methods, representation of the grasped object is insufficient because the image of the grasped object is not used as input. Additionally, there is a drawback that it is impossible to avoid collision during collision prediction as it requires input of the previous image. In this study, we propose the addition of an attention map visualization module for collision prediction and the enhancement of model representation through the use of k-nearest neighbor method. We conduct comparative experiments using standard evaluation metrics for generated text such as BLEU4, METEOR, ROUGE-L, and CIDEr-D. Experimental results show that the proposed method outperforms the baseline method in all evaluation metrics.

Multimodal encoder with gated cross-attention for text-vqa tasks

W Yang, A Ueda, K Sugiura

29th Annual Conference of the Language Processing Society, 1580-1585 2023

Learning to Rank Physical Objects: ランキング学習による物理世界検索エンジン

兼田寛大，神原元就，杉浦孔明

人工知能学会全国大会論文集第 37 回 (2023), 3G1OS24a01-3G1OS24a01 (The Japanese Society for Artificial Intelligence) JSAI2023 ( 0 ) 3G1OS24a01 - 3G1OS24a01 2023

In this study, we focus on the learning-to-rank physical objects task, which involves retrieving target objects from open-vocabulary user instructions in a human-in-the-loop setting. We propose MultiRankIt, which introduces the Crossmodal Noun Phrase Encoder to model the relationship between referring expressions and target bounding box, and the Crossmodal Region Feature Encoder to model the relationship between the target object and its surrounding contextual environment. Our model outperforms the baseline method in terms of mean reciprocal rank and recall@K.

Shared transformer encoder with mask-based 3D model estimation for container mass estimation

T Matsubara, S Otsuki, Y Wada, H Matsuo, T Komatsu, Y Iioka, K Sugiura, ...

ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and … 9142 - 9146 2022

Access to Document (DOI)

生活支援ロボットによる物体操作タスクにおける Funnel UNITER に基づく指示文理解

吉田悠，石川慎太郎，杉浦孔明

人工知能学会全国大会論文集第 36 回 (2022), 2O4GS701-2O4GS701 (The Japanese Society for Artificial Intelligence) JSAI2022 ( 0 ) 2O4GS701 - 2O4GS701 2022

In this study, we develop a multimodal language comprehension model that allows domestic service robots to understand object fetching instructions. We propose a multimodal language understanding model, Funnel UNITER, which gradually reduces the dimensions of the query, key, and value in each transformer layer to reduce the computational cost of self-attention. We also built a new dataset for the multimodal language understanding for fetching instruction (MLU-FI) task called the ALFRED-fetch dataset. Our model outperformed the baseline method in both classification accuracy and training time.

日常タスクにおける将来イベントのクロスモーダル説明文生成

神原元就，杉浦孔明

人工知能学会全国大会論文集第 36 回 (2022), 2O1GS702-2O1GS702 (The Japanese Society for Artificial Intelligence) JSAI2022 ( 0 ) 2O1GS702 - 2O1GS702 2022

In this paper, our aim is to generate a caption about a future event. We propose the Relational Future Captioning Model (RFCM), a crossmodal language generation model for the future captioning task. The RFCM has the Relational Self-Attention Encoder to extract the relationships between events more effectively than the conventional self-attention in transformers. We conducted comparison experiments, and the results show the RFCM outperforms a baseline method on two datasets.

Target Position Prediction Using UNITER Regressor for Understanding Navigation Instructions in Urban Areas

HATANAKA Shumpei, WEI Yang, HOSOMI Naoki, MISU Teruhisa, YAMADA Kentarou, SUGIURA Komei

JSAI Technical Report, SIG-KBS (The Japanese Society for Artificial Intelligence) 126 ( 0 ) 34 - 39 2022

Vision-and-Language Navigation タスクにおける敵対的サブゴール生成

石川慎太朗，杉浦孔明

人工知能学会全国大会論文集第 36 回 (2022), 2O1GS701-2O1GS701 (The Japanese Society for Artificial Intelligence) JSAI2022 ( 0 ) 2O1GS701 - 2O1GS701 2022

In this paper, we focus on a vision-and-language task in which a robot is instructed to execute household tasks. We propose Moment-based Adversarial Training (MAT), which uses two types of moments for perturbation updates in adversarial training. We introduce MAT to the embedding spaces of the instruction, subgoals, and state representations to handle their varieties. We validated our method on the ALFRED benchmark, and the results demonstrated that our method outperformed the baseline method for all the metrics on the benchmark.

Language identifying device and computer program for same, and speech processing device

P Shen, K Sugiura, H Kawai

US Patent App. 17/593,822 2022

Lambda Attention Branch Networks による視覚的説明生成

飯田紡，兼田寛大，平川翼，山下隆義，藤吉弘亘，杉浦孔明

人工知能学会全国大会論文集第 36 回 (2022), 2O1GS703-2O1GS703 (The Japanese Society for Artificial Intelligence) JSAI2022 ( 0 ) 2O1GS703 - 2O1GS703 2022

Explanation generation for transformers enhances accountability for their predictions. However, there have been few studies on generating visual explanations for the transformers that use multidimensional context, such as LambdaNetworks. In this paper, we propose the Lambda Attention Branch Networks, which attend to important regions in detail and generate easily interpretable visual explanations. We also propose the Patch Insertion-Deletion score, an extension of the Insertion-Deletion score, as an effective evaluation metric for images with sparse important regions. Experimental results on two public datasets indicate that the proposed method successfully generates visual explanations.

Flare Transformer: 磁場画像と物理特徴量を用いた太陽フレア予測

兼田寛大，飯田紡，西塚直人，久保勇樹，杉浦孔明

人工知能学会全国大会論文集第 36 回 (2022), 2D6GS201-2D6GS201 (The Japanese Society for Artificial Intelligence) JSAI2022 ( 0 ) 2D6GS201 - 2D6GS201 2022

The prediction of solar flares is essential for reducing the potential damage to social infrastructures that are vital to society. However, predicting solar flares accurately is a very challenging task. In this paper, we propose a solar flare prediction model, Flare Transformer, which handles both images and physical features through the Magnetogram Module and the Sunspot Feature Module. We introduce the transformer attention mechanism to model the temporal relationships. We also introduce a new differentiable loss function to balance the two major metrics of the Gandin-Murphy-Gerrity score and Brier skill score. Comparative experiments using Gandin-Murphy-Gerrity score and true skill statistics as metrics showed that the proposed method achieves better performance than baseline methods and human experts.

Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks.

Motonari Kambara, Komei Sugiura

ICIP 2601 - 2605 2022

Access to Document (DOI)

Moment-based Adversarial Training for Embodied Language Comprehension.

Shintaro Ishikawa, Komei Sugiura

ICPR 4139 - 4145 2022

Access to Document (DOI)

Flare Transformer: Solar Flare Prediction Using Magnetograms and Sunspot Physical Features.

Kanta Kaneda, Yuiga Wada, Tsumugi Iida, Naoto Nishizuka, Yûki Kubo, Komei Sugiura

ACCV (2) 442 - 457 2022

Access to Document (DOI)

Operational solar flare prediction model using Deep Flare Net

N Nishizuka, Y Kubo, K Sugiura, M Den, M Ishii

Earth, Planets and Space 73 (1), 1-12 2021

Predicting and attending to damaging collisions for placing everyday objects in photo-realistic simulations

A Magassouba, K Sugiura, A Nakayama, T Hirakawa, T Yamashita, ...

Advanced Robotics, 1-13 abs/2102.06507 2021

Case Relation Transformer に基づく対象物体及び目標領域の参照表現を含む物体操作指示文生成

神原元就，杉浦孔明

人工知能学会全国大会論文集第 35 回全国大会 (2021), 4J1GS6d05-4J1GS6d05 (The Japanese Society for Artificial Intelligence) 2021 ( 0 ) 4J1GS6d05 - 4J1GS6d05 2021

The purpose of this paper is to extend the dataset based on a cross-modal generative language generation model. We propose a Case Relation Transformer (CRT) that generates a fetching instruction sentence from an image, such as ``Move the blue flip-flop to the lower left box.'' Unlike existing methods, CRT uses Transformer to capture the visual and geometric features of objects in an image. The Case Relation Block allows the CRT to process the object. We conducted comparative experiments and human evaluations. Experimental results showed that CRT outperformed the baseline methods.

生活支援ロボットによる物体配置タスクにおける Transformer PonNet に基づく危険性予測および可視化

植田有咲，平川翼，山下隆義，藤吉弘亘，杉浦孔明

人工知能学会全国大会論文集第 35 回全国大会 (2021), 2J1GS8a03-2J1GS8a03 (The Japanese Society for Artificial Intelligence) 2021 ( 0 ) 2J1GS8a03 - 2J1GS8a03 2021

Placing everyday objects in designated areas, such as placing a glass on a table, is a crucial task for Domestic service robots (DSRs). In this paper, we propose a physical reasoning method about collisions in placement tasks. The proposed method, Transformer PonNet, predicts the probability of a possible collision and visualizes areas involved in the collision. Unlike existing methods, Transformer PonNet can be applied to objects whose models are unavailable. We propose a novel Transformer Perception Branch that handles relationships among features more complex than simple self-attention. We built simulation and physical datasets using a DSR, and validated our method on the datasets. We obtained an accuracy of 82.5% for the physical dataset.

Target-dependent UNITER に基づく対象物体に関する参照表現を含む物体操作指示理解

石川慎太朗，杉浦孔明

人工知能学会全国大会論文集第 35 回全国大会 (2021), 4I2GS7c04-4I2GS7c04 (The Japanese Society for Artificial Intelligence) 2021 ( 0 ) 4I2GS7c04 - 4I2GS7c04 2021

Currently, domestic service robots have an insufficient ability to interact naturally through language. This is because understanding human instructions is complicated by a variety of ambiguities and missing information. Existing methods are insufficient to model reference expressions that specify relationships between objects. In this paper, we propose Target-dependent UNITER, which learns directly the relationship between the target object and other objects by focusing on the relevant regions within an image, instead of the whole image. Our model is validated on two standard datasets, and the results show that Target-dependent UNITER outperforms the baseline method in terms of classification accuracy.

Unified questioner transformer for descriptive question generation in goal-oriented visual dialogue

S Matsumori, K Shingyouchi, Y Abe, Y Fukuchi, K Sugiura, M Imai

Proceedings of the IEEE/CVF International Conference on Computer Vision … abs/2106.15550 2021

Target-Dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots

S Ishikawa, K Sugiura

IEEE Robotics and Automation Letters 6 (4), 8401-8408 2021

CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation.

Aly Magassouba, Komei Sugiura, Hisashi Kawai

IEEE Robotics Autom. Lett. 6 ( 4 ) 6258 - 6265 2021

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions

M Kambara, K Sugiura

IEEE Robotics and Automation Letters 6 (4), 8371-8378 2021

Bilingual Case Relation Transformerに基づく複数言語による物体操作指示文生成

兼田寛大, 神原元就, 杉浦孔明

日本ロボット学会学術講演会予稿集(CD-ROM) 39th 2021

Deep Neural Networks Applied to Predictions of 4-Class Solar Flares and Eruptions

N Nishizuka, M Den, M Ishii, Y Kubo, K Sugiura

43rd COSPAR Scientific Assembly. Held 28 January-4 February 43, 1037 2021

LatteGAN: Visually Guided Language Attention for Multi-Turn Text-Conditioned Image Manipulation

S Matsumori, Y Abe, K Shingyouchi, K Sugiura, M Imai

IEEE Access 9, 160521-160532 2021

配置物体を考慮したAttention機構マルチモーダルネットワークによる衝突予測

河合竹彦, 平川翼, 山下隆義, 藤吉弘亘, 杉浦孔明

日本ロボット学会学術講演会予稿集(CD-ROM) 39th 2021

生活支援ロボットによる物体配置タスクにおける危険性予測および視覚的説明生成

畑中駿平, 上田雄斗, 植田有咲, 平川翼, 山下隆義, 藤吉弘亘, 杉浦孔明

日本ロボット学会学術講演会予稿集(CD-ROM) 39th 2021

物体指示理解タスクにおけるクロスモーダル言語生成に基づくデータ拡張

飯田紡, 九曜克之, 石川慎太朗, 杉浦孔明

日本ロボット学会学術講演会予稿集(CD-ROM) 39th 2021

深層学習を用いた太陽フレア予測モデルによる予報運用評価

西塚直人, 久保勇樹, 杉浦孔明, 田光江, 石井守

日本天文学会年会講演予稿集 2021 2021

ISSN 1347-0639

Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning.

Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Komei Sugiura

International Joint Conference on Neural Networks(IJCNN) (IEEE) 1 - 10 2021

Access to Document (DOI)

Holding apparatus, container provided with tag, object holding program and object holding method

Y Onuma, K Sugiura

US Patent 11,192,242 2021

太陽フレア AI 予報 Deep Flare Net の運用化

西塚直人，久保勇樹，杉浦孔明，田光江，石井守

宇宙航空研究開発機構特別資料: 第 16 回「宇宙環境シンポジウム」講演論文集, 61-64 2020

Research paper (conference, symposium, etc.), Joint Work

Latent-Space Data Augmentation for Visually-Grounded Language Understanding

A Magassouba, K Sugiura, H Kawai

Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing (Springer) 1128 179 - 187 2020

Research paper (other academic), Joint Work

Access to Document (DOI)

A Multimodal Target-Source Classifier With Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects

A Magassouba, K Sugiura, H Kawai

IEEE Robotics and Automation Letters 5 ( 2 ) 532 - 539 2020

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Multimodal Attention Branch Network に基づく把持命令文の生成

小椋忠志，杉浦孔明，平川翼，山下隆義，藤吉弘亘，河井恒

人工知能学会全国大会論文集第 34 回全国大会 (2020), 1Q3GS1105-1Q3GS1105 (The Japanese Society for Artificial Intelligence) 2020 ( 0 ) 1Q3GS1105 - 1Q3GS1105 2020

Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. Nonetheless, one of the main limitations of DSRs is their inability to naturally interact through language. Recently, data-driven approaches have been shown to be effective for tackling this limitation, however, they often require large-scale datasets, which is costly. Based on this background, we aim to perform automatic sentence generation for fetching instructions, e.g., ``Bring me a green tea bottle on the table.'' This is particularly challenging because appropriate expressions depend on the target object, as well as its surroundings. In this paper, we propose a method that generates sentences from visual inputs. Unlike other approaches, the proposed method has multimodal attention branches that utilize subword-level attention and generate sentences based on subword embeddings. In the experiment, we compared the proposed method with a baseline method using four standard metrics in image captioning. Experimental results show that the proposed method outperformed the baseline in terms of these metrics.

Compensation on x-vector for Short Utterance Spoken Language Identification}}

P Shen, X Lu, K Sugiura, S Li, H Kawai

Proc. Odyssey 2020 The Speaker and Language Recognition Workshop, 47-52 2020

Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network

T Ogura, A Magassouba, K Sugiura, T Hirakawa, T Yamashita, ...

IEEE Robotics and Automation Letters abs/2007.04557 2020

A3C における Attention 機構を用いた視覚的説明

板谷英典，平川翼，山下隆義，藤吉弘亘，杉浦孔明

人工知能学会全国大会論文集第 34 回全国大会 (2020), 2J6GS204-2J6GS204 (The Japanese Society for Artificial Intelligence) 2020 ( 0 ) 2J6GS204 - 2J6GS204 2020

Asynchronous Advantage Actor-Critic (A3C) is a representative method of deep reinforcement learning and is possible to solve difficult tasks such as games and robot control. However, it is difficult for deep reinforcement learning including A3C to understand and to explain the reason of action selection. To address this problem, we propose a method called a Mask-Attention A3C, which performs mask processing on feature map of Policy branch using attention map. The propose method can obtain an attention map that is useful for a visual explanation of agent behavior. In the experiment with Atari2600, we compare the scores in each game and demonstrate that the attention map obtained from our method is useful for visual explanation. In addition, we evaluate the explainability of obtained attention map using the scores of each game by changing the attention region.

Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R)

N Nishizuka, Y Kubo, K Sugiura, M Den, M Ishii

The Astrophysical Journal 899 (2), 150 2020

Compensation on x-vector for short utterance spoken language identification

P Shen, X Lu, K Sugiura, S Li, H Kawai

Proc. Odyssey 2020 The Speaker and Language Recognition Workshop, 47-52 2020

Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder–Decoder Network

T Ogura, A Magassouba, K Sugiura, T Hirakawa, T Yamashita, ...

IEEE Robotics and Automation Letters 5 (4), 5945-5952 (Institute of Electrical and Electronics Engineers (IEEE)) 5 ( 4 ) 5945 - 5952 2020

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Instruction understanding system and instruction understanding method

K Sugiura, H Kawai

US Patent 10,796,098 2020

Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target–Source Classification

A Magassouba, K Sugiura, AT Quoc, H Kawai

IEEE Robotics and Automation Letters 4 ( 4 ) 3884 - 3891 2019

Research paper (scientific journal), Joint Work, Accepted

Operation of Solar Flare Prediction by Deep Flare Net.

N Nishizuka, K Sugiura, Y Kubo, M Den, M Ishii

Geophysical Research Abstracts 21 2019

Multimodal attention branch network for perspective-free sentence generation

A Magassouba, K Sugiura, H Kawai

arXiv preprint arXiv:1909.05664 abs/1909.05664 2019

A Multimodal Target-Source Classifier Model for Object Fetching from Natural Language Instructions

A Magassouba, K Sugiura, H Kawai

人工知能学会全国大会論文集 (一般社団法人人工知能学会) 2019 ( 0 ) 2D3E403 - 2D3E403 2019

Research paper (conference, symposium, etc.)

In this paper, we address the fetching task from ambiguous instructions. A typical fetching task consists of picking up a target object specified by ambiguous instructions. We specifically propose a multimodal target-source classifier model (MTCM) that grounds the instructions in the scene. More explicitly, MCTM can predict the likelihood of a target object in addition to the source of this target using linguistic and visual features. Our approach improves the accuracy of the previous state-of-the-art method for target object prediction in fetching task.

Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target-Source Classification.

Aly Magassouba, Komei Sugiura, Anh Trinh Quoc, Hisashi Kawai

IEEE Robotics and Automation Letters 4 ( 4 ) 3884 - 3891 2019

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

生活支援ロボットにおける Generative Adversarial Nets を用いた曖昧な指示の理解

杉浦孔明，河井恒

人工知能学会全国大会論文集第 32 回全国大会 (2018), 2A304-2A304 (The Japanese Society for Artificial Intelligence) 2018 ( 0 ) 2A304 - 2A304 2018

This paper focuses on a multimodal language understanding method for ``Carry and Place'' tasks with domestic service robots. We address the case of ambiguous instructions, that is when the target area is not specified. For instance ``Put away the milk and cereal.'' is a natural instruction where there is ambiguity on the target area, considering daily life environments. Conventionally, this instruction can be disambiguated from a dialogue system, but at the cost of time and cumbersomeness. Instead, we propose a multimodal approach, where the instructions are disambiguated from the robot state and environment context. We develop MultiModal Classifier Generative Adversarial Network (MMC-GAN) to predict the likelihood of the different target areas considering the robot physical limitation and the target clutter. Our approach, MMC-GAN, significantly improves accuracy compared to baseline methods using instructions only or simple deep neural networks.

Solar Flare Prediction Model using Deep Neural Network: Deep Flare Net (DeFN)

N Nishizuka, K Sugiura, Y Kubo, M Den, M Ishii

Solar Heliospheric and INterplanetary Environment (SHINE 2018) 2018

Real-time Solar Flare Prediction with Deep Neural Network using Vector Magnetogram and Chromospheric Brightening

N Nishizuka, M Den, M Ishii, Y Kubo, K Sugiura

42nd COSPAR Scientific Assembly 42 2018

Deep Flare Net (DeFN) model for solar flare prediction

N Nishizuka, K Sugiura, Y Kubo, M Den, M Ishii

The Astrophysical Journal 858 (2), 113 2018

A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions.

Aly Magassouba, Komei Sugiura, Hisashi Kawai

abs/1806.03847 2018

SuMo-SS: Submodular Optimization Sensor Scattering for Deploying Sensor Networks by Drones.

Komei Sugiura

abs/1806.01065 2018

マガスーバアリー, 河井恒,“生活支援ロボットにおける Generative Adversarial Nets を用いた曖昧な指示の理解”

杉浦孔明

人工知能学会全国大会論文集 32, 2A3-04 2018

Spatio-temporal pseudo relevance feedback for scientific data retrieval

Shin'ichi Takeuchi, Komei Sugiura, Yuhei Akahoshi, Koji Zettsu

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING (WILEY-BLACKWELL) 12 ( 1 ) 124 - 131 2017.01

Research paper (scientific journal), Joint Work, Accepted, ISSN 1931-4973

We consider the problem of searching scientific data from vast heterogeneous scientific data repositories. This problem is challenging because scientific data contain relatively little text information compared to other search targets such as web pages. On the other hand, the metadata in scientific data contain other characteristic information such as spatio-temporal information. Although using this information make it possible to improve the search performance, many widely adopted scientific data search engines use this information exclusively for narrowing down search results. In this paper, we propose a novel query generation method using spatial, temporal, and text information based on pseudo relevance feedback. The proposed method generates new spatio-temporal queries from the initial search results. By using these queries, the search results are reranked such that more related results obtain higher rank. The experimental results show that the proposed method outperforms a baseline method when search targets do not have rich text information. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Access to Document (DOI)

機械学習を用いたリアルタイム太陽フレア予測

西塚直人，久保勇樹，杉浦孔明，田光江，亘慎一，石井守

title 宇宙航空研究開発機構特別資料: 第 13 回宇宙環境シンポジウム講演論文集 … 2017

Grounded language understanding for manipulation instructions using GAN-based classification

K Sugiura, H Kawai

2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU … 519 - 524 2017

Access to Document (DOI)

太陽フレア予測への機械学習の応用

西塚直人，杉浦孔明，久保勇樹，田光江，亘慎一，石井守

title 平成 28 年度宇宙科学情報解析シンポジウム Space Science Informatics … 2017

ロボカップ西暦 2050 年を目指して (その 2)……………………… 奥川雅之・伊藤暢浩・岡田浩之・植村渉・高橋友一・杉浦孔明……… 2 (42) 自然言語文からの関係知識ベースの構築 …

用語解説，会告，論文概要，編集後記

知能と情報 (日本知能情報ファジィ学会誌) 29 (2) 2017

ロボカップ西暦 2050 年を目指して (その 2)

奥川雅之，伊藤暢浩，岡田浩之，植村渉，高橋友一，杉浦孔明

知能と情報 29 (2), 42-54 (Japan Society for Fuzzy Theory and Intelligent Informatics) 29 ( 2 ) 42 - 54 2017

ISSN 1347-7986

System and terminal for presenting recommended utterance candidates

K Sugiura, H Okuma, N Kimura, Y Shiga, T Hayashi, E Mizukami

US Patent 9,824,687 2017

Spatio‐temporal pseudo relevance feedback for scientific data retrieval

S Takeuchi, K Sugiura, Y Akahoshi, K Zettsu

IEEJ Transactions on Electrical and Electronic Engineering 12 (1), 124-131 2017

Solar Flare Prediction using the Machine-learning and Operational Evaluation Method

N Nishizuka, K Sugiura, Y Kubo, M Ishii

2017

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations

N Nishizuka, K Sugiura, Y Kubo, M Den, S Watari, M Ishii

Proceedings of the International Astronomical Union 13 (S335), 310-313 2017

Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms

N Nishizuka, K Sugiura, Y Kubo, M Den, S Watari, M Ishii

The Astrophysical Journal 835 (2), 156 2017

Sentence selection based on extended entropy using phonetic and prosodic contexts for statistical parametric speech synthesis

T Nose, Y Arao, T Kobayashi, K Sugiura, Y Shiga

IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (5), 1107 … 25 ( 5 ) 1107 - 1116 2017

Access to Document (DOI)

Machine-learning of Solar Flare Prediction and Operational Evaluation Method

N Nishizuka, K Sugiura, Y Kubo, M Den, S Watari, M Ishii

Solar Heliospheric and INterplanetary Environment (SHINE 2017) 2017

Toward Large-Scale Language Learning by Robots Using Real-World Knowledge and Cloud Robotics Platform

杉浦孔明

計測と制御 : journal of the Society of Instrument and Control Engineers (計測自動制御学会 ; 1962-) 55 ( 10 ) 884 - 889 2016.10

ISSN 0453-4662

CiNii Research

Analysis on Large-scale Human-robot Dialogues Collected by Cloud Platform

杉浦孔明

電子情報通信学会誌 = The journal of the Institute of Electronics, Information and Communication Engineers (電子情報通信学会) 99 ( 6 ) 500 - 504 2016.06

ISSN 0913-5693

CiNii Research

Challenges and Trends in Probabilistic Imitation Learning

杉浦孔明

システム・制御・情報 = Systems, control and information : システム制御情報学会誌 (システム制御情報学会) 60 ( 12 ) 521 - 527 2016

ISSN 0916-1600

CiNii Research

模倣学習における確率ロボティクスの新展開

杉浦孔明

システム/制御/情報 60 (12), 521-527 (THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS) 60 ( 12 ) 521 - 527 2016

ISSN 0916-1600

Analysis of long-term and large-scale experiments on robot dialogues using a cloud robotics platform

K Sugiura, K Zettsu

2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI … 525 - 526 2016

Access to Document (DOI)

劣モジュラ性を利用したドローンによるばらまき型センサ配置

杉浦孔明

人工知能学会全国大会論文集第 30 回全国大会 (2016), 1O5OS22b5-1O5OS22b5 (The Japanese Society for Artificial Intelligence) 2016 ( 0 ) 1O5OS22b5 - 1O5OS22b5 2016

Toward Large-Scale Language Learning by Robots Using Real-World Knowledge and Cloud Robotics Platform

SUGIURA Komei

Journal of The Society of Instrument and Control Engineers (The Society of Instrument and Control Engineers) 55 ( 10 ) 884 - 889 2016

ISSN 0453-4662

ロボットによる大規模言語学習に向けて—実世界知識の利活用とクラウドロボティクス基盤の構築—

杉浦孔明

計測と制御 55 (10), 884-889 2016

ロボカップ@ ホーム: 人と共存するロボットのベンチマークテスト (< 特集> 人工知能研究のベンチマークとは-標準問題・データセット・評価手法-)

杉浦孔明

人工知能 31 (2), 230-236 (人工知能学会 ; 2014-) 31 ( 2 ) 230 - 236 2016

ISSN 2188-2266

CiNii Research

ロボカップ@ ホーム人と共存するロボットのベンチマークテスト, 人工知能

杉浦孔明

2016

ビッグデータの利活用によるロボットの音声コミュニケーション基盤構築

杉浦孔明

電子情報通信学会誌= The journal of the Institute of Electronics, Information … 2016

Special issue on machine learning and data engineering in robotics

K Sugiura, S Behnke, D Kulić, K Yamazaki

Advanced Robotics 30 (11-12), 705-705 30 ( 11-12 ) 705 - 705 2016

Access to Document (DOI)

Space–time multiple regression model for grid-based population estimation in urban areas

KK Lwin, K Sugiura, K Zettsu

International Journal of Geographical Information Science 30 (8), 1579-1593 2016

Space-time multiple regression model for grid-based population estimation in urban areas.

Ko Ko Lwin, Komei Sugiura, Koji Zettsu

30 ( 8 ) 1579 - 1593 2016

Access to Document (DOI)

Solar flare prediction with vector magnetogram and chromospheric brightening using machinelearning

N Nishizuka, Y Kubo, K Sugiura, M Den, S Watari, M Ishii

Japan Geoscience Union Meeting 2016

Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

BT Ong, K Sugiura, K Zettsu

Neural Computing and Applications 27 (6), 1553-1566 27 ( 6 ) 1553 - 1566 2016

Access to Document (DOI)

記号創発ロボティクスによる人間機械コラボレーション基盤創成

長井隆行，谷口忠大，尾形哲也，岩橋直人，杉浦孔明，稲邑哲也， ...

第 19 回クラウドネットワークロボット研究会, 23-27 2015

A cloud robotics approach towards dialogue-oriented robot speech

K Sugiura, Y Shiga, H Kawai, T Misu, C Hori

Advanced Robotics 29 (7), 449-456 29 ( 7 ) 449 - 456 2015

Access to Document (DOI)

Constrained region selection method based on configuration space for visualization in scientific dataset search

S Takeuchi, K Sugiura, Y Akahoshi, K Zettsu

2015 IEEE International Conference on Big Data (Big Data), 2191-2200 2191 - 2200 2015

Access to Document (DOI)

大規模異分野データ横断検索における時空間スコア計算法

竹内伸一, 杉浦孔明, 赤星祐平, 是津耕司

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2015 ( 0 ) 1G3OS08b4 - 1G3OS08b4 2015

近年、科学データのオープン化の取り組みに加え，政府や行政機関が保有する公共データを公開し利用を促進する動きも広まっている．筆者らは横断的オープンデータ検索システムの開発を行っており，テキスト情報と時空間情報とを考慮したスコアリングによって分野横断的なデータ検索を実現している．本報告では検索およびクエリ拡張時に必要となるクエリとデータとの間の時空間スコア計算法について述べる．

Rospeex: A cloud robotics platform for human-robot spoken dialogues

K Sugiura, K Zettsu

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems … 6155 - 6160 2015

Access to Document (DOI)

RoboCup@ Home: Analysis and results of evolving competitions for domestic and service robots

L Iocchi, D Holz, J Ruiz-del-Solar, K Sugiura, T Van Der Zant

Artificial Intelligence 229, 258-281 229 258 - 281 2015

Access to Document (DOI)

RoboCup 2014: Robot World Cup XVIII [papers from the 18th Annual RoboCup International Symposium, João Pessoa, Brazil, July 15

Reinaldo A. C. Bianchi, H. Levent Akin, Subramanian Ramamoorthy, Komei Sugiura

RoboCup 8992 2015

ISSN 978-3-319-18614-6

Access to Document (DOI)

Geovisualization and correlation analysis between geotagged Twitter and JMA rainfall data: Case of heavy rain disaster in Hiroshima

KK Lwin, K Zettsu, K Sugiura

2015 2nd IEEE International Conference on Spatial Data Mining and … 71 - 76 2015

Access to Document (DOI)

Constrained Greedy Reranking Based on Spatial Diversity for Scientific Data Retrieval

S Takeuchi, K Sugiura, Y Akahoshi, K Zettsu

2015

Entropy-based sentence selection for speech synthesis using phonetic and prosodic contexts

T Nose, Y Arao, T Kobayashi, K Sugiura, Y Shiga, A Ito

Sixteenth Annual Conference of the International Speech Communication … 3491 - 3495 2015

Deep Recurrent Neural Network による環境モニタリングデータの予測

杉浦孔明，是津耕司

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2015 ( 0 ) 2C3OS06b3 - 2C3OS06b3 2015

ISSN 1347-9881

統計的音声合成におけるエントロピーに基づく大規模学習文セット構築の評価

能勢隆，荒生侑介，小林隆夫，杉浦孔明，志賀芳則

電子情報通信学会技術研究報告; 信学技報 115 (184), 39-44 2015

杉浦孔明，岩橋直人，芳賀麻誉美

観光と情報: 観光情報学会誌 10 (1), 15-24 (観光情報学会) 10 ( 1 ) 15 - 24 2014

ISSN 1349-919X

CiNii Research

観光スポット推薦アプリ「京のおすすめ」を用いた長期実証実験

杉浦孔明，岩橋直人，芳賀麻誉美，堀智織

観光情報学会誌 10 (1), 15-24 2014

A new dimension for robocup@ home: human-robot interaction between virtual and real worlds

JTC Tan, T Inamura, Y Hagiwara, K Sugiura, T Nagai, H Okada

Proceedings of the 2014 ACM/IEEE international conference on Human-robot … 332 - 332 2014

Access to Document (DOI)

Dynamic pre-training of deep recurrent neural networks for predicting environmental monitoring data

BT Ong, K Sugiura, K Zettsu

2014 IEEE International Conference on Big Data (Big Data), 760-765 760 - 765 2014

Access to Document (DOI)

大規模長時間のインタラクションを可能にするロボカップ@ ホームシミュレーションの構想とその基盤技術

稲邑哲也，萩原良信，杉浦孔明，長井隆行，岡田浩之

知能と情報 26 (3), 698-709 (Japan Society for Fuzzy Theory and Intelligent Informatics) 26 ( 3 ) 698 - 709 2014

ISSN 1347-7986

Research on high level human-robot interaction systems that aims skill acquisition, concept learning, modification of dialogue strategy and so on requires large-scaled experience database based on social and embodied interaction experiments. However, if we use real robot systems, costs for development of robots and performing many experiments will be too huge. If we choose virtual robot simulator, limitation arises on embodied interaction between virtual robots and real users. We thus propose an enhanced robot simulator that enables multiuser to connect to central simulation world, and enables users to join the virtual world through immersive user interface. In this paper, we propose a framework of RoboCup@Home simulation based on such technology. We also show base technology and feasibility of the RoboCup@Home simulation system.

大規模異分野データ横断検索における時空間情報を用いた疑似適合性フィードバック

竹内伸一, 赤星祐平, Ong Bun Theang, 杉浦孔明, 是津耕司

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2014 ( 0 ) 2E5OS25b2 - 2E5OS25b2 2014

個々に収集、分析されてきた様々な科学データに対し、分野横断的に活用することで新たな科学的知見を発見しようとする取り組みがなされており、その一環として蓄積された膨大なデータ群から関連するデータを検索するシステムの必要性が高まっている。本稿では科学データ検索システムの性能向上のためテキスト情報に加えて時空間情報を併用した、科学データ検索のためのクエリ拡張手法を提案する。

大規模異分野データ横断検索における時空間情報を用いた疑似適合性フィードバック

竹内伸一，赤星祐平，杉浦孔明，是津耕司

人工知能学会全国大会論文集第 28 回全国大会 (2014), 2E5OS25b2-2E5OS25b2 2014

Team er@ sers 2014 in the@ home league team description paper

H Okada, T Omori, T Shimotomai, N Iwahashi, K Sugiura, T Nagai, ...

Technical report, Tamagawa University, Tokyo and Kyoto University and … 2014

Spatio-temporal pseudo relevance feedback for large-scale and heterogeneous scientific repositories

S Takeuchi, Y Akahoshi, BT Ong, K Sugiura, K Zettsu

2014 IEEE International Congress on Big Data, 669-676 669 - 676 2014

Access to Document (DOI)

Service-Controlled Networking: アプリケーション構やユーザ要求に連動したネットワーク制御技術

木全崇，杉浦孔明，董冕雄，是津耕司

2014

RPD-HMM に基づく逐次動作生成による物体操作の模倣学習

杉浦孔明，是津耕司

人工知能学会全国大会論文集第 28 回全国大会 (2014), 1I5OS09b5-1I5OS09b5 (The Japanese Society for Artificial Intelligence) 2014 ( 0 ) 1I5OS09b5 - 1I5OS09b5 2014

サービスロボットにとって物体操作は必要不可欠であるが，時々刻々と変化する状況に応じた動作を事前にプログラムするコストは大きい．これまで我々はRPD-HMMを用いた模倣学習手法を提案してきたが，逐次的に動作が生成できないという問題があった．本論文ではRPD-HMMから生成された最尤軌道を基準として逐次的に動作生成を行なう手法を提案する．

Quality-of-experience (QoE) in emerging mobile social networks

M Dong, T Kimata, K Sugiura, K Zettsu

IEICE TRANSACTIONS on Information and Systems 97 (10), 2606-2612 (一般社団法人電子情報通信学会) 97 ( 10 ) 2606 - 2612 2014

ISSN 0916-8532

Mobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs. To the best of our knowledge, this paper is the first article to address QoE issues in emerging MSNs. In this paper, we first present a comprehensive investigation on recent advances in MSNs as well as QoE issues addressed in various types of applications and networks. From the lessons learned from the literature, then we propose a future research direction of QoE in MSNs.

On RoboCup@Home - Past, Present and Future of a Scientific Competition for Service Robots.

Dirk Holz, Javier Ruiz-del-Solar, Komei Sugiura, Sven Wachsmuth

RoboCup 686 - 697 2014

Access to Document (DOI)

On RoboCup@ Home–past, present and future of a scientific competition for service robots

D Holz, J Ruiz-del-Solar, K Sugiura, S Wachsmuth

Robot Soccer World Cup, 686-697 2014

Non-monologue HMM-based speech synthesis for service robots: A cloud robotics approach

K Sugiura, Y Shiga, H Kawai, T Misu, C Hori

2014 IEEE International Conference on Robotics and Automation (ICRA), 2237-2242 2237 - 2242 2014

Access to Document (DOI)

Location-aware contents distribution platform based on Probe Vehicle Systems

M Sato, K Sugiura

2014 Seventh International Conference on Mobile Computing and Ubiquitous … 2014

rospeex : A Cloud-based Spoken Language Communication Toolkit for ROS

SUGIURA Komei, HORI Chiori, ZETTSU Koji

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 (The Institute of Electronics, Information and Communication Engineers) 113 ( 248 ) 7 - 10 2013.10

ISSN 0913-5685

Stand-alone speech recognition/synthesis engines deployed on robots have drawbacks in accuracy and quality compared with cloud-based speech engines. In this paper, we propose "rospeex"', a novel cloud-based speech communication toolkit for ROS (Robot Operating System). It provides robots with high-performance speech recognition/synthesis capabilities with easy-to-use APIs. We have released the cloud-based service, so that researchers can use it without any cost/authentication.

CiNii Research

階層型評価構造に基づく観光スポット推薦システムの構築と長期実証実験

杉浦孔明，岩橋直人，芳賀麻誉美，堀智織

観光情報学第 8 回研究発表会講演論文集, 9-12 2013

Complementary Integration of Heterogeneous Crowd-Sourced Datasets for Enhanced Social Analytics

R Lee, KS Kim, K Sugiura, K Zettsu, Y Kidawara

2013 IEEE 14th International Conference on Mobile Data Management 2, 234-243 234 - 243 2013

Access to Document (DOI)

ロボカップ@ ホームにおける日用品マニピュレーション

杉浦孔明，長井隆行

日本ロボット学会誌 31 (4), 370-375 (The Robotics Society of Japan) 31 ( 4 ) 370 - 375 2013

ISSN 02891824

サービスロボットのための非モノローグ HMM による音声合成

杉浦孔明，志賀芳則，河井恒，翠輝久，堀智織

第 31 回ロボット学会学術講演会資料, pp. 2C1–02 2013

Utterance Classification Using Linguistic and Non-linguistic Information for Network-Based Speech-to-Speech Translation Systems

K Sugiura, R Lee, H Kashioka, K Zettsu, Y Kidawara

2013 IEEE 14th International Conference on Mobile Data Management 2, 212-216 212 - 216 2013

Access to Document (DOI)

rospeex: クラウド型音声コミュニケーションを実現する ROS 向けツールキット

杉浦孔明，堀智織，是津耕司

信学技報 (CNR2013-10) 113, 7-10 2013

Human-robot interaction between virtual and real worlds: Motivation from robocup@ home

JTC Tan, T Inamura, K Sugiura, T Nagai, H Okada

International Conference on Social Robotics, 239-248 239 - 248 2013

Access to Document (DOI)

Development of robocup@ home simulation towards long-term large scale hri

T Inamura, JTC Tan, K Sugiura, T Nagai, H Okada

Robot Soccer World Cup, 672-680 672 - 680 2013

Access to Document (DOI)

Development of Dialogue Systems "'Kyo-no Hanna' and 'Kyo no Osusume'"

翠輝久, 水上悦雄, 杉浦孔明

情報通信研究機構季報 (情報通信研究機構) 58 ( 3 ) 25 - 29 2012.09

ISSN 1349-3191

CiNii Research

DP-1-1 RoboCup@Home : A Benchmark Test for Domestic Service Robots

Sugiura Komei

Proceedings of the IEICE General Conference (The Institute of Electronics, Information and Communication Engineers) 2012 ( 2 ) "SS - 21"-"SS-22" 2012.03

CiNii Research

観光案内への音声対話システムの活用

柏岡秀紀，翠輝久，水上悦雄，杉浦孔明，岩橋直人，堀智織

デジタルプラクティス 3 (4), 254-261 3 ( 4 ) 254 - 261 2012

本論では，情報通信研究機構で開発，公開しているスマートフォン用音声対話システムAssisTra，観光スポット推薦システム京のおすすめについて述べる．AssisTraの公開に先立ちシステムの実稼働性，有効性，ユーザビリティ等の検証，問題点の洗い出し，ユーザの行動データ収集を目的として延べ100名を対象に実験を行った．その概要と結果についても報告する．さらに，京のおすすめについては，システム構築のために4000人のアンケートを実施した．その結果を基に，ユーザが問いかけやすい観点を見つけている．代表的な観点として「気分」「体験」「雰囲気」「特徴」に着目することにより，観光スポットを推薦するシステムである．

CiNii Research

3-4 対話処理システム (はんなのガイド・京のおすすめ) の開発

翠輝久，水上悦雄，杉浦孔明，岩橋直人

情報通信研究機構研究報告 58 (3.4), 25-29 (National Institute of Information and Communications Technology) 58 ( 3 ) 25 - 29 2012

ISSN 2187-767X

An extended mobile manipulation robot learning novel objects

T Nakamura, M Attamimi, K Sugiura, T Nagai, N Iwahashi, T Toda, ...

Journal of Intelligent & Robotic Systems 66 (1), 187-204 2012

ロボット対話: 実世界情報を用いたコミュニケーションの学習 (< 特集> 記号創発ロボティクス)

杉浦孔明

人工知能学会誌 27 (6), 580-586 (人工知能学会) 27 ( 6 ) 580 - 586 2012

ISSN 0912-8085

CiNii Research

ロボカップ道しるべ: ロボカップ@ ホームリーグ

杉浦孔明

情報処理 53 (3), 250-261 (情報処理学会) 53 ( 3 ) 250 - 261 2012

ISSN 0447-8053

CiNii Research

ロボカップ@ ホームにおけるロボット向けベンチマークテスト構築の試み

杉浦孔明

2012

コミュニケーションするロボットは創れるか-記号創発システムへの構成論的アプローチコミュニケーションするロボットは創れるか-記号創発システムへの構成論的アプローチ, 2010

杉浦孔明

人工知能学会誌 27 (6), 580-586 2012

アッタミミムハンマド, 杉浦孔明, 長井隆行, 岩橋直人, 戸田智基, 岡田浩之, 大森隆司:“拡張モバイルマニピュレーションのための新規物体の学習”

中村友昭

日本ロボット学会誌 30 (2), 213-224 2012

Robot dialogue: robots that learn physically-situated communication

K Sugiura

J Jpn Soc Artif Intell 27, 580-586 2012

Learning novel objects for extended mobile manipulation

T Nakamura, K Sugiura, T Nagai, N Iwahashi, T Toda, H Okada, T Omori

Journal of Intelligent & Robotic Systems 66 (1-2), 187-204 66 ( 1-2 ) 187 - 204 2012

Access to Document (DOI)

Learning Novel Objects for Domestic Service Robots

M Attamimi, T Nakamura, T Nagai, K Sugiura, N Iwahashi

The Future of Humanoid Robots: Research and Applications, 257 2012

3-4 Development of Dialogue Systems ‘“Kyo-no Hanna’and ‘K yo no Osusume’”

T Misu, E Mizukami, K Sugiura, N Iwahashi

Journal of the National Institute of Information and Communications … 2012

拡張モバイルマニピュレーションのための新規物体の学習

中村友昭，杉浦孔明，長井隆行，岩橋直人，戸田智基，岡田浩之， ...

日本ロボット学会誌 30 (2), 213-224 (The Robotics Society of Japan) 30 ( 2 ) 213 - 224 2012

ISSN 02891824

It is convenient for users to teach novel objects to a domestic service robot with a simple procedure. In this paper, we propose a method for learning the images and names of these objects shown by the users. The object images are segmented out from cluttered scenes by using motion attention. Phoneme recognition and voice conversion are used for the speech recognition and synthesis of the object names that are out of vocabulary. In the experiments conducted with 120 everyday objects, we have obtained an accuracy of 91% for object recognition and an accuracy of 82% for word recognition. Furthermore, we have implemented the proposed method on a physical robot, DiGORO, and evaluated its performance by using RoboCup@Home's “Supermarket” task. The results have shown that DiGORO has outperformed the highest score obtained in the RoboCup@Home 2009 competition.

物体操作タスクにおける HMM を用いた障害物回避動作の生成

山内祐輝

第 12 回計測自動制御学会システムインテグレーション部門講演会, 2011, 1614-1617 2011

Learning Novel Object for Extended Mobile Manipulation

T Nakamura

J. Intelligent & Robotic, Systems, 1-18 2011

Situated spoken dialogue with robots using active learning

K Sugiura, N Iwahashi, H Kawai, S Nakamura

Advanced Robotics 25 (17), 2207-2232 25 ( 17 ) 2207 - 2232 2011

ISSN 01691864

Online Learning of Bayes Risk-Based Optimization of Dialogue Management for Document Retrieval Systems with Speech Interface

T Misu, K Sugiura, T Kawahara, K Ohtake, C Hori, H Kashioka, ...

Spoken Dialogue Systems Technology and Design, 29-52 29 - 52 2011

Access to Document (DOI)

Motion generation by reference-point-dependent trajectory HMMs

K Sugiura, N Iwahashi, H Kashioka

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems … 350 - 356 2011

Access to Document (DOI)

Modeling spoken decision support dialogue and optimization of its dialogue strategy

T Misu, K Sugiura, T Kawahara, K Ohtake, C Hori, H Kashioka, H Kawai, ...

ACM Transactions on Speech and Language Processing (TSLP) 7 (3), 1-18 7 ( 3 ) 10:1 - 10:18 2011

Access to Document (DOI)

Learning, generation and recognition of motions by reference-point-dependent probabilistic models

K Sugiura, N Iwahashi, H Kashioka, S Nakamura

Advanced Robotics 25 (6-7), 825-848 25 ( 6 ) 825 - 848 2011

ISSN 01691864

Learning, Generation, and Recognition of Motions by Reference-Point-Dependent Probabilistic Models

SUGIURA Komei

Advanced Robotics Vol.25, No.6-7 825 - 848 2011

Accepted

CiNii Research

言語獲得ロボットによる発話理解確率の推定に基づく物体操作対話

杉浦孔明，岩橋直人，柏岡秀紀，中村哲

日本ロボット学会誌 28 (8), 978-988 (The Robotics Society of Japan) 28 ( 8 ) 978 - 988 2010

ISSN 02891824

This paper proposes a method that generates motions and utterances in an object manipulation dialogue task. The proposed method integrates belief modules for speech, vision, and motions into a probabilistic framework so that a user's utterances can be understood based on multimodal information. Responses to the utterances are optimized based on an integrated confidence measure function for the integrated belief modules. Bayesian logistic regression is used for the learning of the confidence measure function. The experimental results revealed that the proposed method reduced the failure rate from 12% down to 2.6% while the rejection rate was less than 24%.

相談型対話のモデル化と対話戦略の最適化

翠輝久，杉浦孔明，大竹清敬，堀智織，柏岡秀紀，河井恒，中村哲

研究報告音声言語情報処理 (SLP) 2010 (23), 1-6 (情報処理学会) 82 W1 - W6 2010

ISSN 09196072

本稿では，ユーザがシステムから情報提示・推薦を受けながら候補を選択する相談型の音声対話システムの枠組みについて述べる．嗜好に合った候補を選択する際には，多くの要因を考慮する必要がある．システムを利用するユーザは，そのような要因の全てを必ずしも把握しているわけではないため，システムはユーザに対して情報推薦を行い，知識のギャップを埋める必要がある．本研究では，このように複数の候補の中からユーザに適した候補を選択する相談型対話のモデルを提案する．京都観光案内タスクにおいて，観光スポットを決定する対話システムの実装を行い，被験者実験を行った．さらに，被験者実験で得られた対話データから，ユーザのシミュレータを作成し，自然政策勾配法を用いた強化学習を用いて対話戦略の最適化を行った．This paper addresses a spoken dialogue framework that helps users make decisions. Various decision criteria are involved in selecting from a given set of alternatives. Users often do not have a definite goal or criteria for selection, and thus the system has to bridge the knowledge gap and also recommend an appropriate alternative together with the reason for the recommendation through a dialogue. In this paper, we present a model for such consulting dialogue. In order to evaluate the model, we implement a trial sightseeing guidance system and conduct a user experiment. Then, we optimize the dialogue strategy through reinforcement learning with a natural policy gradient approach using a user simulator trained from the collected dialogue data.

CiNii Research

Active learning for generating motion and utterances in object manipulation dialogue tasks

K Sugiura, N Iwahashi, H Kawai, S Nakamura

2010 AAAI Fall Symposium Series 2010

日用品を扱う物体操作の模倣学習による動作の言語化

杉浦孔明，岩橋直人，柏岡秀紀，中村哲

人工知能学会全国大会論文集第 24 回全国大会 (2010), 1J1OS136-1J1OS136 (The Japanese Society for Artificial Intelligence) 2010 ( 0 ) 1J1OS136 - 1J1OS136 2010

生活環境で人間を支援するロボットへの期待が高まっている．ロボカップ＠ホームなど生活環境で日用品を扱うロボットが見られるようになってきたが，物体操作はヒューリスティックな手法により実現されている．本論文では，ユーザが日用品をハンドリングする軌道から，動作の確率モデルを統計学習する手法を提案する．本手法と音声処理を統合することにより，物体操作の学習・認識・生成を行う対話ロボットについて報告する．

Active learning of confidence measure function in robot language acquisition framework

K Sugiura, N Iwahashi, H Kashioka, S Nakamura

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems … 1774 - 1779 2010

Access to Document (DOI)

Statistical imitation learning in sequential object manipulation tasks

K Sugiura, H Kashioka, N Iwahashi, S Nakamura

INTECH Open Access Publisher 2010

Simultaneous design of the sensory morphology and controller of mobile robots

K Sugiura, H Kawakami, O Katai

Electrical Engineering in Japan 172 (1), 48-57 2010

Robots that learn to communicate: A developmental approach to personally and physically situated human-robot conversations

N Iwahashi, K Sugiura, R Taguchi, T Nagai, T Taniguchi

2010 AAAI Fall Symposium Series 2010

Robot-directed speech detection using multimodal semantic confidence based on speech, image, and motion

X Zuo, N Iwahashi, R Taguchi, S Matsuda, K Sugiura, K Funakoshi, ...

2010 IEEE International Conference on Acoustics, Speech and Signal … 2458 - 2461 2010

Access to Document (DOI)

Modeling spoken decision making dialogue and optimization of its dialogue strategy

T Misu, K Sugiura, K Ohtake, C Hori, H Kashioka, H Kawai, S Nakamura

Proceedings of the 11th Annual Meeting of the Special Interest Group on … 221 - 224 2010

Learning novel objects using out-of-vocabulary word segmentation and object extraction for home assistant robots

M Attamimi, A Mizutani, T Nakamura, K Sugiura, T Nagai, N Iwahashi, ...

2010 IEEE International Conference on Robotics and Automation, 745-750 745 - 750 2010

Access to Document (DOI)

Dialogue strategy optimization to assist user's decision for spoken consulting dialogue systems

T Misu, K Sugiura, K Ohtake, C Hori, H Kashioka, H Kawai, S Nakamura

2010 IEEE Spoken Language Technology Workshop, 354-359 354 - 359 2010

Access to Document (DOI)

Detection of Robot-Directed Speech by Situated Understanding in Physical Interaction

左祥，岩橋直人，田口亮，松田繁樹，杉浦孔明，船越孝太郎，中野幹生， ...

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2010 ( 0 ) 1J1OS134 - 1J1OS134 2010

In this paper, we propose a novel method for a robot to detect robot-directed speech from other speech: to distinguish speech that users speak to a robot from speech that users speak to other people or to himself. The originality of this work is the introduction of Multimodal Semantic Confidence measure, which is used for domain classification of input speech based on deciding whether the speech can be interpreted as a feasible action under the current physical situation in an object manipulation task. This measure is calculated by integrating speech, object, and motion confidence with weightings that are optimized by logistic regression. Then we integrate this measure with gaze tracking, and conduct experiments under conditions of natural human-robot interactions. Experimental results show that the proposed method achieved a high performance of 94% and 96% in average recall and precision rates for robot-directed speech detection.

Detecting robot-directed speech by situated understanding in physical interaction

X Zuo, N Iwahashi, K Funakoshi, M Nakano, R Taguchi, S Matsuda, ...

Information and Media Technologies 5 (4), 1314-1326 (一般社団法人人工知能学会) 25 ( 6 ) 670 - 682 2010

ISSN 1346-0714

In this paper, we propose a novel method for a robot to detect robot-directed speech: to distinguish speech that users speak to a robot from speech that users speak to other people or to themselves. The originality of this work is the introduction of a multimodal semantic confidence (MSC) measure, which is used for domain classification of input speech based on the decision on whether the speech can be interpreted as a feasible action under the current physical situation in an object manipulation task. This measure is calculated by integrating speech, object, and motion confidence with weightings that are optimized by logistic regression. Then we integrate this measure with gaze tracking and conduct experiments under conditions of natural human-robot interactions. Experimental results show that the proposed method achieves a high performance of 94% and 96% in average recall and precision rates, respectively, for robot-directed speech detection.

Detecting robot-directed speech by situated understanding in object manipulation tasks

X Zuo, N Iwahashi, R Taguchi, K Funakoshi, M Nakano, S Matsuda, ...

19th International Symposium in Robot and Human Interactive Communication … 608 - 613 2010

Access to Document (DOI)

言語獲得ロボットによる発話理解確率の推定に基づく物体操作対話確認発話

SUGIURA Komei, IWAHASHI Naoto

日本ロボット学会誌 Vol.28, No.8 978 - 988 2010

Accepted

CiNii Research

Bayesian learning of confidence measure function for generation of utterances and motions in object manipulation dialogue task

K Sugiura, N Iwahashi, H Kashioka, S Nakamura

Tenth Annual Conference of the International Speech Communication Association 2483 - 2486 2009

対話戦略最適化としての発話理解確信度の能動学習

杉浦孔明, 岩橋直人

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 2009 ( 0 ) 1F2OS79 - 1F2OS79 2009

本論文では，ユーザとロボットが対話を行うことにより，ユーザ発話に対する発話理解確信度を学習する手法を提案する．発話理解確信度は，音声・画像・動作・文法を統合した確率モデルにより表現され，実環境でユーザの教示に基づく統計的学習により獲得される．本論文では，物体操作タスクを行うロボットに提案手法を実装し，発話理解確信度の効率的な学習のために最適な発話を行わせた実験について報告する．

対話戦略最適化としての発話理解確信度の能動学習

杉浦孔明，岩橋直人

人工知能学会全国大会論文集第 23 回全国大会 (2009), 1F2OS79-1F2OS79 2009

Robots that learn to converse: Developmental approach to situated language processing

N Iwahashi, R Taguchi, K Sugiura, K Funakoshi, M Nakano

Proceedings of International Symposium on Speech and Language Processing … 2009

Simultaneous Design Method of the Sensory Morphology and Controller of Mobile Robots

SUGIURA Komei, KAWAKAMI Hiroshi, KATAI Osamu

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan) 128 ( 7 ) 1154 - 1161 2008.07

ISSN 03854221

This paper proposes a method that automatically designs the sensory morphology of a mobile robot. The proposed method employs two types of adaptations -ontogenetic and phylogenetic- to optimize the sensory morphology of the robot. In ontogenetic adaptation, reinforcement learning searches for the optimal policy which is highly dependent on the sensory morphology. In phylogenetic adaptation, a genetic algorithm is used to select morphologies with which the robot can learn tasks faster. Our proposed method is applied to the design of the sensory morphology of a line-following robot. We carried out simulation experiments to compare the design solution with a hand-coded robot. The results of the experiments revealed that our robot outperforms a hand-coded robot in terms of the line-following accuracy and learning speed, although our robot has fewer sensors than the hand-coded one. We also manufactured a physical robot using the design solution. The experimental results revealed that this physical robot uses its morphology effectively and outperforms the hand-coded robot.

音声からの未登録語切り出しと画像からの物体抽出の統合による新規物体の学習

杉浦孔明，水谷了，中村友昭，長井隆行，岩橋直人，岡田浩之，大森隆司

第 26 回日本ロボット学会学術講演会論文集, 2008 2008

移動ロボットにおけるセンサ形態と制御系の同時設計法

杉浦孔明，川上浩司，片井修

電気学会論文誌 C (電子・情報・システム部門誌) 128 (7), 1154-1161 2008

Constructive approach to role-reversal imitation through unsegmented interactions

T Taniguchi, N Iwahashi, K Sugiura, T Sawaragi

Journal of Robotics and Mechatronics 20 (4), 567 20 ( 4 ) 567 - 577 2008

Access to Document (DOI)

参照点に依存した確率モデルを用いた連続動作の認識と生成

Sugiura Komei, Iwahashi Naoto

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence) 8 ( 0 ) 372 - 372 2008

ロボカップ@ ホームにおける音声対話技術

岡田浩之，大森隆司，岩橋直人，長井隆行，杉浦孔明

人工知能学会全国大会論文集第 22 回全国大会 (2008), 368-368 (The Japanese Society for Artificial Intelligence) 8 ( 0 ) 368 - 368 2008

Simultaneous Design Method of the Sensory Morphology and Controller of Mobile Robots

K Sugiura, H Kawakami, O Katai

IEEJ Transactions on Electronics, Information and Systems 128, 1154-1161 2008

Motion recognition and generation by combining reference-point-dependent probabilistic models

K Sugiura, N Iwahashi

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems … 852 - 857 2008

Access to Document (DOI)

自律ロボットにおける形態に基づく解釈系の構築

杉浦孔明

京都大学 (Kyoto University) 2007

移動ロボットのための複数のタスク環境における学習に適した形態の設計法

杉浦孔明，川上浩司

[C] 平成 19 年電気学会電子・情報・システム部門大会講演論文集, 118-124 2007

Learning object-manipulation verbs for human-robot communication

K Sugiura, N Iwahashi

Proceedings of the 2007 workshop on Multimodal interfaces in semantic … 2007

ライントレーサにおける形態と制御系の同時設計: センサ進化が学習に与える影響の考察

西川徳宏，杉浦孔明，塩瀬隆之，川上浩司，片井修

自律分散システム・シンポジウム資料= SICE Symposium on Decentralized Autonomous … 19 183 - 188 2007

CiNii Research

Construction of robotic body schema by extracting temporal information from sensory inputs

K Sugiura, D Matsubara, O Katai

2006 SICE-ICASE International Joint Conference, 302-307 2006

Exploiting interaction between sensory morphology and learning

K Sugiura, M Akahane, T Shiose, K Shimohara, O Katai

2005 IEEE International Conference on Systems, Man and Cybernetics 1, 883-888 883 - 888 2005

Access to Document (DOI)

Co-evolution of Sensors and Controllers

K Sugiura, T Shiose, H Kawakami, O Katai

IEEJ Transactions on Electronics, Information and Systems 124 (10), 1938-1943 (一般社団法人電気学会) 124 ( 10 ) 1938 - 1943 2004

ISSN 03854221

In this paper we investigate the evolutionary development of embodied agents that evolve not only control mechanisms but also the sensitivity and temporal resolution of their sensors. In our system, agents can adapt their sensory parameters to their morphology and task environments, while no explicit cost for sensors is used. We have carried out two kinds of experiments in which agents perform phototactic and pursuit/evading behaviors. The experimental results indicate that the sensors and controller co-evolve in an agent through interaction with the environments.

Evolution of rewriting rule sets using string-based tierra

K Sugiura, H Suzuki, T Shiose, H Kawakami, O Katai

European Conference on Artificial Life, 69-77 69 - 77 2003

Access to Document (DOI)

The multi-sharing mobile remote monitoring system-the four layer object management model and practical system

Y Murata, K Sugiura, S Ishihara, T Mizuno

17th International Conference on Advanced Information Networking and … 2003

　 Previous page - Next page 　