We investigate how collaborative guidance can be realized in multimodal virtual environments for dynamic tasks involving motor control. Haptic guidance in our context can be defined as any form offorce/tactile feedback that the computer generates to help a user execute a task in a faster, more accurate, and subjectively more
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Since the strict separation of working spaces of humans and robots has experienced a softening due to recent robotics research achievements, close interaction of humans and robots comes rapidly into reach. In this context, physical human– robot interaction raises a number of questions regarding a desired intuitive robot behavior. The
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We explore the effect of laughter perception and response in terms of engagement in human-robot interaction. We designed two distinct experiments in which the robot has two modes: laughter responsive and laughter non-responsive. In responsive mode, the robot detects laughter using a multimodal real-time laughter detection module and invokes laughter
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We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user.
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Hand-drawn objects usually consist of multiple semantically meaningful parts. For example, a stick figure consists of a head, a torso, and pairs of legs and arms. Efficient and accurate identification of these subparts promises to significantly improve algorithms for stylization, deformation, morphing and animation of 2D drawings. In this paper,
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Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics.We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method requires neither a statistical body shape model
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‘Serious games’ are becoming extremely relevant to individuals who have specific needs, such as children with an Autism Spectrum Condition (ASC). Often, individuals with an ASC have difficulties in interpreting verbal and non-verbal communication cues during social interactions. The ASC-Inclusion EU-FP7 funded project aims to provide children who have an
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We present HapTable; a multi–modal interactive tabletop that allows users to interact with digital images and objects through natural touch gestures, and receive visual and haptic feedback accordingly. In our system, hand pose is registered by an infrared camera and hand gestures are classified using a Support Vector Machine (SVM)
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Head-nods and turn-taking both significantly contribute conversational dynamics in dyadic interactions. Timely prediction and use of these events is quite valuable for dialog management systems in human-robot interaction. In this study, we present an audio-visual prediction framework for the head-nod and turntaking events that can also be utilized in real-time
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This paper addresses the problem of evaluating engagement of the human participant by combining verbal and nonverbal behaviour along with contextual information. This study will be carried out through four different corpora. Four different systems designed to explore essential and complementary aspects of the JOKER system in terms of paralinguistic/linguistic
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