語用論(Pragmatics)を考える

Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

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Human-Robot Interaction: A Survey

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

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Multimodal fusion for multimedia analysis: a survey

Many researchers use Wizard of Oz (WoZ) as an experimental technique, but there are methodological concerns over its use, and no comprehensive criteria on how to best employ it. We systematically review 54 WoZ experiments published in the primary HRI publication venues from 2001 -- 2011. Using criteria proposed by Fraser and Gilbert (1991), Green et al. (2004), Steinfeld et al. (2009), and Kelley (1984), we analyzed how researchers conducted HRI WoZ experiments. Researchers mainly used WoZ for verbal (72.2%) and non-verbal (48.1%) processing. Most constrained wizard production (90.7%), but few constrained wizard recognition (11%). Few reported measuring wizard error (3.7%), and few reported pre-experiment wizard training (5.4%). Few reported using WoZ in an iterative manner (24.1%). Based on these results we propose new reporting guidelines to aid future research.

Wizard of Oz studies in HRI: a systematic review and new reporting guidelines

This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit

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A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

In this paper we present our ongoing work in building technologies for natural multimodal human-robot interaction. We present our systems for spontaneous speech recognition, multimodal dialogue processing and visual perception of a user, which includes the recognition of pointing gestures as well as the recognition of a person's head orientation. Each of the components is described in the paper and experimental results are presented. In order to demonstrate and measure the usefulness of such technologies for human-robot interaction, all components have been integrated on a mobile robot platform and have been used for real-time human-robot interaction in a kitchen scenario.

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Natural human-robot interaction using speech, head pose and gestures

In this paper, we present our work in building technologies for natural multimodal human-robot interaction. We present our systems for spontaneous speech recognition, multimodal dialogue processing, and visual perception of a user, which includes localization, tracking, and identification of the user, recognition of pointing gestures, as well as the recognition of a person's head orientation. Each of the components is described in the paper and experimental results are presented. We also present several experiments on multimodal human-robot interaction, such as interaction using speech and gestures, the automatic determination of the addressee during human-human-robot interaction, as well on interactive learning of dialogue strategies. The work and the components presented here constitute the core building blocks for audiovisual perception of humans and multimodal human-robot interaction used for the humanoid robot developed within the German research project (Sonderforschungsbereich) on humanoid cooperative robots.

Enabling Multimodal Human–Robot Interaction for the Karlsruhe Humanoid Robot

This paper presents an architecture for fusion of multimodal input streams for natural interaction with a humanoid robot as well as results from a user study with our system. The presented fusion architecture consists of an application independent parser of input events, and application specific rules. In the presented user study, people could interact with a robot in a kitchen scenario, using speech and gesture input. In the study, we could observe that our fusion approach is very tolerant against falsely detected pointing gestures. This is because we use speech as the main modality and pointing gestures mainly for disambiguation of objects. In the paper we also report about the temporal correlation of speech and gesture events as observed in the user study.

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Implementation and evaluation of a constraint-based multimodal fusion system for speech and 3D pointing gestures

Future life pictures humans having intelligent humanoid robotic systems taking part in their everyday life Thus researchers strive to supply robots with an adequate artificial intelligence in order to achieve a natural and intuitive interaction between human being and robotic system Within the German Humanoid Project we focus on learning and cooperating multimodal robotic systems In this paper we present a first cognitive architecture for our humanoid robot: The architecture is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand Perception, learning, planning of actions, motor control, and human-like communication play an important role in the robotic system and are embedded step by step in our architecture

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Hartwig Holzapfel

Papers

Natural human-robot interaction using speech, head pose and gestures

Enabling Multimodal Human–Robot Interaction for the Karlsruhe Humanoid Robot

Implementation and evaluation of a constraint-based multimodal fusion system for speech and 3D pointing gestures

A cognitive architecture for a humanoid robot: a first approach

A dialogue approach to learning object descriptions and semantic categories