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Cooperation and Interaction between Human and Humanoid Robots through Integration of Symbolic Expressions and Sensorimotor Patterns
https://ir.soken.ac.jp/records/3588
https://ir.soken.ac.jp/records/35887f1cb887-2a0a-4256-8011-de02999797af
| 名前 / ファイル | ライセンス | アクション |
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要旨・審査要旨 (341.6 kB)
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本文 (5.2 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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| 公開日 | 2013-06-12 | |||||
| タイトル | ||||||
| タイトル | Cooperation and Interaction between Human and Humanoid Robots through Integration of Symbolic Expressions and Sensorimotor Patterns | |||||
| タイトル | ||||||
| タイトル | Cooperation and Interaction between Human and Humanoid Robots through Integration of Symbolic Expressions and Sensorimotor Patterns | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| 著者名 |
奥野, 敬丞
× 奥野, 敬丞 |
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| フリガナ |
オクノ, ケイスケ
× オクノ, ケイスケ |
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| 著者 |
OKUNO, Keisuke
× OKUNO, Keisuke |
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| 学位授与機関 | ||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||
| 学位名 | ||||||
| 学位名 | 博士（情報学） | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 総研大甲第1555号 | |||||
| 研究科 | ||||||
| 値 | 複合科学研究科 | |||||
| 専攻 | ||||||
| 値 | 17 情報学専攻 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2012-09-28 | |||||
| 学位授与年度 | ||||||
| 値 | 2012 | |||||
| 要旨 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | This paper describes a stochastic framework for intelligent humanoid robots, which can cooperate and interact with humans through integra- tion of symbolic expressions and sensorimotor patterns. The research is divided into 4 steps. Contributions of the each research step are: 1) an estimation method of sensorimotor patterns of others without having pre- defined user speciffic model in advance through interaction between self and other, 2) a method to dynamically modify displaying motion pat- terns and to bind the motions with symbol expressions according to per- formance of human-learners, in order for conveying slight differences in motions, where robotic system coaches humans motions, 3) analysis and modeling of human-coaches' use of motions and symbolic expressions how they change them dynamically according to learners performances, and 4) demonstration of the feasibility of the robotic motion coaching system, which integrated the methods proposed in step 1) and 2), and the models gained in step 3), through experiments of actual sport coaching tasks for beginners resulted in improvements in motion learning. In the Chapter 1, The main stream of robotics researches are introduced as improvement in individual physical ability. Then, importance of in- telligence of binding symbol expressions and unobservable sensorimotor patterns, and intelligence to estimate the sensorimotor patterns from ob- servable motions are discussed from interaction point of view. In the Chapter 2, related works are introduced in various fields such as Robotics, Conversation Analysis, Human-Agent Interaction, Skill and Sports Science, and Anticipation of Intention of Others from neuroscience and cognitive psychology point of view. Then, the chapter addresses chal- lenges from the perspective of required functions for the research. After the discussion of the approach for the resolution method, the Proto-symbol Space method is introduced as a basic tool for the proposed methods. The Chapter 3 describes an estimation method of sensorimotor patterns of others from motion observation. An approach is to bridge sensorimotor experience, or the Proto-symbol Spaces, between the self and the other. The sensorimotor experience for each are represented by the Proto-symbol Spaces for each in the research. This approach would result in estimation error due to physical condition diffierence between the self and the other. To clear this problem, a method is proposed in order for adaptive acquisition of Proto-symbol Space of other by sharing motion patterns and using open questions asking the others' sensing status described by symbols. Simulation demonstrates that it is possible to estimate sensorimotor patterns of others with 10- 20% errors, even when estimation target motions are not in database. In the second half of the chapter, I discusses about a method to estimate others' symbol conversion strategy from sensor patterns. The method uses closed questions asking comparative evaluation of sets of shared motions. The simulation demonstrates that the method can estimate the symbol conversion strategy properly by sharing prepared sets of motions and using the closed questions. The Chapter 4 describes a proposing method for dynamic modification of motion demonstration and for binding the motions with symbol ex- pressions according to performance of human-learners. This method can convey slight diffierences between learning target motions demonstrated by a coach and motions performed by learners. Feasibility of the method is demonstrated through experiments of actual sport coaching tasks for be- ginners by using a robotic coaching system. The robotic system coaches human-learners tennis forehand swing, by using emphatic motions and adverbial expressions generated from the proposing method. The experi- ments resulted in improvements in motion learning. However, it was not possible to confirm whether either emphatic motions and/or adverbial expressions is a contribution factor or not. In the Chapter 5, I discuss about experiments for modeling how human- coaches use emphatic motions and adverbial expressions. In the experi- ments, human-coaches were asked to coach a robot-learner tennis forehand swing, by using the emphatic motions and adverbial expressions. Analysis of the results leads to models; two Adverbial Expression Use Models and two Emphatic Motion Use Models. In the Chapter 6, I attempt to integrate the methods proposed in Chapter 3 and 4, and the models obtained in Chapter 5. At first, I discuss about integration of the robotic motion coaching system from Chapter 4 and the models gained from Chapter 5. I then discuss a possible integration of the method to estimate sensorimotor patterns from the Chapter 3, the robotic motion coaching system from Chapter 4, and the models gained from Chapter 5. I demonstrated the feasibility of the robotic motion coaching system inte- grated with one of the EMU-Model and one of the AEU-Model, by experi- ments of a tennis forehand swing coaching task for beginners. I confirmed that the EMU-Model and the AEU-Model contribute to improvement in motion learning. It is demonstrated that value output by the EMU-Model is a contribution factor by a statistic analysis. I also found there is an improvement in motion learning when using the AEU-Models. However, even though I found positive contribution of the adverbial expressions for the improvement in motion learning, it is not able to decide whether the adverbial expressions chosen by using the AEU-Model is a contribution factor or not. The thesis is then concluded in the Chapter 7. |
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| 値 | 有 | |||||
