Papers - SUGIURA, Komei

Robot dialogue: robots that learn physically-situated communication

K Sugiura

J Jpn Soc Artif Intell 27, 580-586  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

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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

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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

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

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

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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

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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

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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

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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

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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

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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

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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%.

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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

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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.

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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

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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

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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

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<p>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.</p>

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