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

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. "

    Full-text · Article · Sep 2015
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    • "For example, motion patterns learned from observations of humans can be used to make the navigation among people more effective [6], [7]. In addition, knowledge of people's behavior can support devising ways for robots to approach people [8], find appropriate places to offer services [9] or chose a place for waiting [10]. It is not clear if models of behavior based on data collected at one specific point of time apply at some different time. "
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    ABSTRACT: Knowledge about space usage from variables such as density and walking speed could support a variety of service applications. However, there is not much knowledge on how the usage of space changes during extended periods of time and what affects the changes. We have installed a person tracking system in a large area of a shopping center and collected pedestrian data over a year. In this paper, we analyze the collected data to find the changes in pedestrian density and speed, percentage of children, and pedestrian trajectories. The changes from day to day, as well as during the day are examined, and a number of factors that affect them are identified. This is in turn used in the prediction of the state of the space using a Gaussian process model.
    Full-text · Article · Dec 2014 · IEEE Transactions on Human-Machine Systems
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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.
    Full-text · Article · Sep 2014
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