Untrained VR users adapt their multimodal instruction strategies to the spatial clarity of the task, using explicit speech with concrete anchors and implicit speech with prolonged pointing in ambiguous settings.
Multimodal Dataset of Human-Robot Hugging Interaction
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abstract
A hug is a tight embrace and an expression of warmth, sympathy and camaraderie. Despite the fact that a hug often only takes a few seconds, it is filled with details and nuances and is a highly complex process of coordination between two agents. For human-robot collaborative tasks, it is necessary for humans to develop trust and see the robot as a partner to perform a given task together. Datasets representing agent-agent interaction are scarce and, if available, of limited quality. To study the underlying phenomena and variations in a hug between a person and a robot, we deployed Baxter humanoid robot and wearable sensors on persons to record 353 episodes of hugging activity. 33 people were given minimal instructions to hug the humanoid robot for as natural hugging interaction as possible. In the paper, we present our methodology and analysis of the collected dataset. The use of this dataset is to implement machine learning methods for the humanoid robot to learn to anticipate and react to the movements of a person approaching for a hug. In this regard, we show the significance of the dataset by highlighting certain features in our dataset.
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Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
Untrained VR users adapt their multimodal instruction strategies to the spatial clarity of the task, using explicit speech with concrete anchors and implicit speech with prolonged pointing in ambiguous settings.