Recognition of haptic interaction patterns in dyadic joint object manipulation

Madan, Cigil Ece and Kucukyilmaz, Ayse and Sezgin, T.M. and Basdogan, C. (2015) Recognition of haptic interaction patterns in dyadic joint object manipulation. Haptics, IEEE Transactions on, 8 (1). pp. 54-66. ISSN 1939-1412

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Abstract

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.

Keywords:haptic interfaces, human-robot interaction, support vector machines, SVM classifier, autonomous robot, dyadic joint object manipulation, haptic interaction pattern recognition, human interaction patterns, human-human cooperation, human-human-interaction dataset, interaction patterns, physical tasks, support vector machine, Pattern recognition, Virtual environments, Behavior recognition, feature evaluation and selection, haptic collaboration, haptics-enabled virtual environments, machine learning, physical human-X interaction
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:29368
Deposited On:28 Nov 2017 20:20

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