通过检测f型队形使机器人遵守社会规范

A. Kollakidou, Lakshadeep Naik, Oskar Palinko, L. Bodenhagen
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引用次数: 3

摘要

机器人在与人类共享的环境中导航应该考虑社会结构和相互作用。社会群体的识别对机器人来说是一个挑战,因为它包含了许多学科。我们提出了一种分层聚类方法,将个体分组为独立会话组(FSCS),利用他们的位置和方向。在SALSA数据集上对该方法进行了评价,F1得分为0.94。该算法还评估了可扩展性,并在移动机器人上实现,试图检测社会群体并参与互动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Robots to Adhere to Social Norms by Detecting F-Formations
Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.
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