  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.
