Conference Paper

How to Approach Humans?-Strategies for Social Robots to Initiate Interaction-

DOI: 10.1145/1514095.1514117 Conference: Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, HRI 2009, La Jolla, California, USA, March 9-13, 2009
Source: DBLP


ABSTRACT This paper proposes a model,of approach,behavior with which a robot can initiate conversation with people who,are walking. We developed,the model by learning,from the failures in a simplistic approach,behavior ,used in a ,real shopping ,mall. Sometimes people were unaware of the robot’s presence, even when it spoke tothem. Sometimes, people were not sure whether the robot was really trying to start a conversation, and they did not start talking with it even ,though ,they displayed interest. To prevent ,such failures, our model includes the following functions: predicting the walking behavior of people, choosing a target person, planning its approaching path, and nonverbally indicating its intention to initiate ,a conversation. ,The approach ,model ,was implemented,and ,used in a ,real shopping ,mall. The field trial demonstrated,that our model ,significantly improves ,the robot’s performance,in initiating conversations. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces-Interaction styles General Terms

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    • "The following section (III) details the control design in order to equitably approach and join a detected group. Taking into account the fact that a frontal approach is the better solution [9], a robot should approach a group without entering the O-space of the interaction. Another point to consider is that a robot should reveal its intention of imminent approach to the group members. "

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    • "Our approach uses the notion of social distances to find an appropriate location to place the wheelchair when the user wants to interact with the other people in a respectful and comfortable way. [10] proposes a model of approaching behavior with which a robot can initiate a conversation with people who are walking. To prevent failures, their model includes a prediction of the walking behavior of people, choosing a target person, planning its approaching path, and nonverbally indicating its intention to initiate a conversation. "
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    ABSTRACT: Approaching a group of humans is an important navigation task. Although many methods have been proposed to avoid interrupting groups of people engaged in a conversation, just a few works have considered the proper way of joining those groups. Research in the field of social sciences have proposed geometric models to compute the best points to join a group. In this article we propose a method to use those points as possible destinations when driving a robotic wheelchair. Those points are considered together with other possible destinations in the environment such as points of interest or typical static destinations defined by the user's habits. The intended destination is inferred using a Dynamic Bayesian Network that takes into account the contextual information of the environment and user's orders to compute the probability for each destination.
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    • "Sisbot and his colleagues developed a path-planning algorithm that considers people's comfort [23]. Satake and his colleagues developed an approaching behavior, in which anticipation of the target person's motion was indispensable [22] "
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    ABSTRACT: Walking side by side is a common situation when we go from one place to another with another person while talking. Our previous study reported a basic mechanism for side-by-side walking, but in the previous model it was crucial that each agent knew where he or she was going, i.e. the route to the destination. However, we have recognized the need to model the situation where one of the agents does not know the destination. The extended model presented in this work has two states: leader-follower state and collaborative state. Depending on whether the follower agent has obtained a reliable estimate of the route to follow, the walking agents transition between the two states. The model is calibrated with trajectories acquired from pairs of people walking side by side, and then it is tested in a human-robot interaction scenario. The results demonstrate that the new extended model achieves better side-by-side performance than a standard method without knowledge of the subgoal.
    Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction; 03/2014
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